14.2.13 Nearest Neighbor Classification

Cover, T.M., and Hart, P.E.,
Nearest Neighbor Pattern Classification,
IT(13), No. 1, January 1967, pp. 21-27. BibRef 6701

Cover, T.M.,
Estimation by the Nearest-Neighbor Rule,
IT(14), No. 1, January, 1968, pp. 50-55. BibRef 6801

Hart, P.E.,
The Condensed Nearest Neighbor Rule,
IT(14), No. 5, May 1968, pp. 515-516. BibRef 6805

Gates, G.W.,
The Reduced Nearest Neighbor Rule,
IT(18), No. 5, May 1972, pp. 431-433. BibRef 7205

Friedman, J.H., Baskett, F., and Shustek, L.J.,
An Algorithm for Finding Nearest Neighbor,
TC(24), October 1975, pp. 1000-1006. BibRef 7510

Dudani, S.A.,
The Distance-Weighted k-Nearest-Neighbor Rule,
SMC(6), No. 4, April 1976, pp. 325-327. BibRef 7604

Yunck, T.P.,
A Technique to Identify Nearest Neighbors,
SMC(6), No. 10, October 1976, pp. 678-683. BibRef 7610

Devijver, P.A.[Pierre A.],
A note on ties in voting with the k-NN rule,
PR(10), No. 4, 1978, pp. 297-298.
Elsevier DOI 0309
BibRef

Dasarathy, B., White, L.J.[Lee J.],
A characterization of nearest-neighbor rule decision surfaces and a new approach to generate them,
PR(10), No. 1, 1978, pp. 41-46.
Elsevier DOI 0309
BibRef

Mack, Y.P., and Rosenblatt, M.,
Multivariate k-Nearest Neighbor Density Estimates,
J. Multivariate Analysis(9), 1979, pp. 1-15. BibRef 7900

Srihari, S.N.[Sargur N.], Snabb, T.[Thomas], White, L.J.[Lee J.],
An algorithm for determining identity of nearest-neighbor and potential function decision rules,
PR(12), No. 5, 1980, pp. 293-299.
Elsevier DOI 0309
BibRef

Koplowitz, J.[Jack], Brown, T.A.[Thomas A.],
On the relation of performance to editing in nearest neighbor rules,
PR(13), No. 3, 1981, pp. 251-255.
Elsevier DOI 0309
BibRef

Short, R., and Fukanaga, K.,
The Optimal Distance Measure for Nearest Neighbor Classification,
IT(27), 1981, pp. 622-627. BibRef 8100
Earlier:
A New Nearest Neighbor Distance Measure,
ICPR80(81-86). BibRef

Fukunaga, K., and Hostetler, L.D.,
The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition,
IT(21), No. 1, January 1975, pp. 32-40. A Hill-Climbing algorithm for the Estimate the mode of a density function. Mean-Shift. BibRef 7501

Fukunaga, K., and Hostetler, L.D.,
K-Nearest-Neighbor Bayes Risk Estimation,
IT(21), No. 5, May 1975, pp. 258-293. Bayes. BibRef 7505

Fukunaga, K., and Flick, T.E.,
An Optimal Global Nearest Neighbor Metric,
PAMI(6), No. 3, May, 1984, pp. 314-318.
See also Classification Error for a Very Large Number of Classes. BibRef 8405

Fukunaga, K., and Flick, T.E.,
The 2-NN Rule for More Accurate NN Risk Estimation,
PAMI(7), No. 1, January 1985, pp. 107-112. BibRef 8501

Fukunaga, K., and Flick, T.E.,
A Test of the Gaussian-ness of a Data Set Using Clustering,
PAMI(8), No. 2, March 1986, pp. 240-247. BibRef 8603

Fukunaga, K., and Hummels, D.M.,
Bias of Nearest Neighbor Error Estimates,
PAMI(9), No. 1, January 1987, pp. 103-112. BibRef 8701

Fukunaga, K., and Hummels, D.M.,
Bayes Error Estimation Using Parzen and K-NN Procedures,
PAMI(9), No. 5, September 1987, pp. 634-643. BibRef 8709

Fukunaga, K., and Hummels, D.M.,
Leave-One-Out Procedures for Nonparametric Error Estimates,
PAMI(11), No. 4, April 1989, pp. 421-423.
IEEE DOI BibRef 8904

Goin, J.E.,
Classification Bias of the K-Nearest Neighbor Algorithm,
PAMI(6), No. 3, May, 1984, pp. 379-381. BibRef 8405

Katajainen, J.[Jyrki], Nevalainen, O.S.[Olli S.],
Computing relative neighbourhood graphs in the plane,
PR(19), No. 3, 1986, pp. 221-228.
Elsevier DOI 0309
BibRef

Loizou, G., and Maybank, S.J.,
The Nearest Neighbor and the Bayes Error Rates,
PAMI(9), No. 2, March 1987, pp. 254-262. BibRef 8703

Mazzola, S.,
A K-nearest neighbor-based method for the restoration of damaged images,
PR(23), No. 1-2, 1990, pp. 179-184.
Elsevier DOI 0401
BibRef

Myles, J.P., Hand, D.J.,
The Multi-Class Metric Problem in Nearest Neighbour Discrimination Rules,
PR(23), No. 11, 1990, pp. 1291-1297.
Elsevier DOI BibRef 9000

Patrick, E.A.[Edward A.],
The outcome advisor®,
PR(23), No. 12, 1990, pp. 1427-1439.
Elsevier DOI 0401
An outgrowth of statistical pattern recognition and the Patrick-Fisher Generalized K-nearest Neighbor Decision Rule. BibRef

Weiss, S.M.,
Small Sample Error Rate Estimation for K-NN Classifiers,
PAMI(13), No. 3, March 1991, pp. 285-289.
IEEE DOI BibRef 9103

Buturovic, L.J.,
Improving k-nearest neighbor density and error estimates,
PR(26), No. 4, April 1993, pp. 611-616.
Elsevier DOI 0401
BibRef

Buturovic, L.J., Markovic, M.Z.,
Improving k-nearest neighbor Bayes error estimates,
ICPR92(II:470-473).
IEEE DOI 9208
BibRef

Smith, S.P.,
Threshold Validity for Mutual Neighborhood Clustering,
PAMI(15), No. 1, January 1993, pp. 89-92.
IEEE DOI Analysis of some of the problems in clustering. BibRef 9301

Hattori, K.[Kazuo], Torii, Y.[Yasunobu],
Effective algorithms for the nearest neighbor method in the clustering problem,
PR(26), No. 5, May 1993, pp. 741-746.
Elsevier DOI 0401
BibRef

Hastie, T., Tibshirani, R.,
Discriminant Adaptive Nearest-Neighbor Classification,
PAMI(18), No. 6, June 1996, pp. 607-616.
IEEE DOI 9607
BibRef

Zakarauskas, P., Ozard, J.M.,
Complexity Analysis for Partitioning Nearest-Neighbor Searching Algorithms,
PAMI(18), No. 6, June 1996, pp. 663-668.
IEEE DOI 9607
BibRef

Lu, G.W., Yu, F.T.S.,
Pattern-Classification Using a Joint Transform Correlator Based Nearest-Neighbor Classifier,
OptEng(35), No. 8, August 1996, pp. 2162-2170. 9609
BibRef

Hamamoto, Y., Uchimura, S., Tomita, S.,
A Bootstrap Technique for Nearest-Neighbor Classifier Design,
PAMI(19), No. 1, January 1997, pp. 73-79.
IEEE DOI 9702
BibRef

Dasarathy, B.V.,
Nearest Unlike Neighbor (NUN): An Aid to Decision Confidence Estimation,
OptEng(34), No. 9, September 1995, pp. 2785-2792. BibRef 9509
Earlier:
Fuzzy Understanding of Neighborhoods with Nearest Unlike Neighbor Sets,
SPIE(2493), Application of Fuzzy Logic Technology II, April 1995, pp. 34-43. BibRef

Dasarathy, B.V.,
Minimal Consistent Subset (MCS) Identification for Optimal Nearest Neighbor Decision Systems Design,
SMC(24), No. 3, March 1994, pp. 511-517. BibRef 9403

Dasarathy, B.V., and Sheela, B.V.,
A Composite Classifier System Design: Concepts and Methodology,
PIEEE(67), No. 5, May 1979, pp. 708-713. BibRef 7905
Earlier:
Design of Composite Classifier Systems in Imperfectly Supervised Environments,
PRIP79(71-78). BibRef

Dasarathy, B.V.,
There Goes the Neighborhood: An ALIEN Identification Approach to Recognition in Partially Exposed Environments,
ICPR80(91-93). BibRef 8000

Stoica, I.,
A Time-Optimal Multiple-Query Nearest-Neighbor Algorithm on Meshes with Multiple Broadcasting,
PRAI(9), 1995, pp. 663-677. BibRef 9500

Vidal Ruiz, E.,
An Algorithm for Finding Nearest Neighbors in (Approximately) Constant Time,
PRL(4), 1986, pp. 145-157. BibRef 8600

Zeng, G., Dubes, R.C.,
A Test for Spatial Randomness Based on K-NN Distances,
PRL(3), 1985, pp. 85-91. BibRef 8500

Zeng, G.Z.[Guang-Zhou], Dubes, R.C.[Richard C.],
A comparison of tests for randomness,
PR(18), No. 2, 1985, pp. 191-198.
Elsevier DOI 0309
This paper compares five distance-based statistics (Hopkins, Cox-Lewis, Eberhardt and two T-square statistics) which reflect the degree to which a set of d-dimensional points is random. BibRef

Miller, G.L., Teng, S.H., Thurston, W., Vavasis, S.A.,
Separators for Sphere-Packings and Nearest-Neighbor Graphs,
JACM(44), No. 1, January 1997, pp. 1-29. 9704
BibRef

van der Heiden, R., Groen, F.C.A.,
The Box-Cox Metric for Nearest-Neighbor Classification Improvement,
PR(30), No. 2, February 1997, pp. 273-279.
Elsevier DOI 9704
BibRef
And: Correction: PR(30), No. 7, July 1997, pp. 1251-1251. BibRef

Decaestecker, C.[Christine],
Finding Prototypes for Nearest-Neighbor Classification by Means of Gradient Descent and Deterministic Annealing,
PR(30), No. 2, February 1997, pp. 281-288.
Elsevier DOI 9704
BibRef

Paget, R., Longstaff, I.D.,
Extracting the Cliques from a Neighborhood System,
VISP(144), No. 3, June 1997, pp. 168-170. 9708
BibRef

Salvador-Sanchez, J.[Jose], Pla, F.[Filiberto], Ferri, F.J.[Francesc J.],
Prototype Selection for the Nearest-Neighbor Rule Through Proximity Graphs,
PRL(18), No. 6, June 1997, pp. 507-513. 9710
BibRef
Earlier:
Using Geometric Information in Prototype Selection for the Nearest Neighbour Rule,
BMVC96(Poster Session 1). 9608
Universitat Jaume I and Universitat de Valencia BibRef

Salvador-Sanchez, J.[Jose], Pla, F.[Filiberto], Ferri, F.J.[Francesc J.],
Improving the k-NCN classification rule through heuristic modifications,
PRL(19), No. 13, November 1998, pp. 1165-1170. BibRef 9811

Jozwik, A., Serpico, S., Roli, F.,
A Parallel Network of Modified 1-NN and K-NN Classifiers: Application to Remote Sensing Image Classification,
PRL(19), No. 1, January 1998, pp. 57-62. 9807
BibRef

Chen, Z.M.[Zhen-Min], van Ness, J.W.[John W.],
Characterizations of Nearest and Farthest Neighbor Algorithms by Clustering Admissibility Conditions,
PR(31), No. 10, October 1998, pp. 1573-1578.
Elsevier DOI 9808
BibRef

Chen, Z.M.[Zhen-Min], van Ness, J.W.[John W.],
Space-contracting, space-dilating, and positive admissible clustering algorithms,
PR(27), No. 6, June 1994, pp. 853-857.
Elsevier DOI 0401
BibRef

Lipowezky, U.[Uri],
Selection of the optimal prototype subset for 1-NN classification,
PRL(19), No. 10, 31 August 1998, pp. 907-918. BibRef 9808

Hattori, K.[Kazuo], Takahashi, M.[Masahito],
A new nearest-neighbor rule in the pattern classification problem,
PR(32), No. 3, March 1999, pp. 425-432.
Elsevier DOI BibRef 9903

Ricci, F.[Francesco], Avesani, P.[Paolo],
Data Compression and Local Metrics for Nearest Neighbor Classification,
PAMI(21), No. 4, April 1999, pp. 380-384.
IEEE DOI BibRef 9904

Bermejo, S.[Sergio], Cabestany, J.[Joan],
Adaptive soft k-nearest-neighbour classifiers,
PR(33), No. 12, December 2000, pp. 1999-2005.
Elsevier DOI 0008
BibRef
And: Abstract: PR(32), No. 12, December 1999, pp. 2077-2079.
Elsevier DOI BibRef

Hattori, K.[Kazuo], Takahashi, M.[Masahito],
A new edited k-nearest neighbor rule in the pattern classification problem,
PR(33), No. 3, March 2000, pp. 521-528.
Elsevier DOI 0001
BibRef

Kuncheva, L.I.[Ludmila I.], Jain, L.C.[Lakhmi C.],
Nearest neighbor classifier: Simultaneous editing and feature selection,
PRL(20), No. 11-13, November 1999, pp. 1149-1156. 0001
BibRef

Ferrari, A., Borgatti, M., Guerrieri, R.,
A complete system for NN classification based on a VLSI array processor,
PR(33), No. 12, December 2000, pp. 2083-2093.
Elsevier DOI 0008
BibRef
Earlier:
A VLSI Array Processor Accelerator for K-NN Classification,
ICPR96(IV: 723-727).
IEEE DOI 9608
(Univ. di Bologna, I) BibRef

Li, S.Z.[Stan Z.], Chan, K.L.[Kap Luk], Wang, C.L.[Chang-Liang],
Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval,
PAMI(22), No. 11, November 2000, pp. 1335-1339.
IEEE DOI 0012
Retrieval. BibRef

Zhou, Z., Li, S.Z., Chan, K.L.,
A Theoretical Justification of Nearest Feature Line Method,
ICPR00(Vol II: 759-762).
IEEE DOI 0009
BibRef

Paredes, R.[Roberto], Vidal, E.[Enrique],
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems,
PRL(21), No. 12, November 2000, pp. 1027-1036. 0011
BibRef

Paredes, R.[Roberto], Vidal, E.[Enrique],
Learning prototypes and distances: A prototype reduction technique based on nearest neighbor error minimization,
PR(39), No. 2, February 2006, pp. 180-188.
Elsevier DOI 0512
BibRef
Earlier: ICPR04(III: 442-445).
IEEE DOI 0409
BibRef
Earlier:
Weighting Prototypes: A New Editing Approach,
ICPR00(Vol II: 25-28).
IEEE DOI 0009
BibRef

Paredes, R.[Roberto], Vidal, E.[Enrique],
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error,
PAMI(28), No. 7, July 2006, pp. 1100-1110.
IEEE DOI 0606
BibRef

Villegas, M.[Mauricio], Paredes, R.[Roberto],
Score Fusion by Maximizing the Area under the ROC Curve,
IbPRIA09(473-480).
Springer DOI 0906
BibRef
Earlier:
Simultaneous learning of a discriminative projection and prototypes for Nearest-Neighbor classification,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Liu, C.L.[Cheng-Lin], Nakagawa, M.[Masaki],
Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition,
PR(34), No. 3, March 2001, pp. 601-615.
Elsevier DOI 0101
Evaluation. Handwritten Characters. BibRef

Pal, N.R., Ghosh, S.,
Some classification algorithms integrating Dempster-Shafer theory of evidence with the rank nearest neighbor rules,
SMC-A(31), No. 1, January 2001, pp. 59-66.
IEEE Top Reference. 0104
BibRef

Singh, S.[Sameer], Haddon, J.[John], Markou, M.[Markos],
Nearest-neighbour classifiers in natural scene analysis,
PR(34), No. 8, August 2001, pp. 1601-1612.
Elsevier DOI 0105
BibRef

Nock, R.[Richard], Sebban, M.[Marc],
An improved bound on the finite-sample risk of the nearest neighbor rule,
PRL(22), No. 3-4, March 2001, pp. 407-412.
Elsevier DOI 0105
BibRef

Huang, Y.S., Chiang, C.C., Shieh, J.W., Grimson, W.E.L.,
Prototype optimization for nearest-neighbor classification,
PR(35), No. 6, June 2002, pp. 1237-1245.
Elsevier DOI 0203
BibRef
Earlier:
Constructing Optimized Prototypes for Nearest Neighbor Classifiers,
ICPR00(Vol II: 17-20).
IEEE DOI 0009
BibRef

Wu, Y.Q.[Ying-Quan], Ianakiev, K.[Krassimir], Govindaraju, V.[Venu],
Improved k-nearest neighbor classification,
PR(35), No. 10, October 2002, pp. 2311-2318.
Elsevier DOI 0206
BibRef

Ho, S.Y.[Shinn-Ying], Liu, C.C.[Chia-Cheng], Liu, S.[Soundy],
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm,
PRL(23), No. 13, November 2002, pp. 1495-1503.
Elsevier DOI 0206
BibRef

Huang, X.L.[Xiao-Lu], Zhu, Q.M.[Qiu-Ming],
A pseudo-nearest-neighbor approach for missing data recovery on Gaussian random data sets,
PRL(23), No. 13, November 2002, pp. 1613-1622.
Elsevier DOI 0206
BibRef

Domeniconi, C.[Carlotta], Peng, J.[Jing], Gunopulos, D.[Dimitrios],
Locally Adaptive Metric Nearest-Neighbor Classification,
PAMI(24), No. 9, September 2002, pp. 1281-1285.
IEEE Abstract. 0209
BibRef
Earlier:
Adaptive Metric Nearest Neighbor Classification,
CVPR00(I: 517-522).
IEEE DOI 0005
BibRef

Domeniconi, C., Yan, B.[Bojun],
Nearest neighbor ensemble,
ICPR04(I: 228-231).
IEEE DOI 0409
BibRef

Zhang, P., Peng, J., Domeniconi, C.,
Kernel Pooled Local Subspaces for Classification,
SMC-B(35), No. 3, June 2005, pp. 489-502.
IEEE DOI 0508
BibRef

Yu, G.X.[Guo-Xian], Zhang, G.J.[Guo-Ji], Domeniconi, C.[Carlotta], Yu, Z.W.[Zhi-Wen], You, J.[Jane],
Semi-supervised classification based on random subspace dimensionality reduction,
PR(45), No. 3, March 2012, pp. 1119-1135.
Elsevier DOI 1111
Graph construction; Semi-supervised classification; Random subspaces; Dimensionality reduction; Ensembles of classifiers BibRef

Peng, J.[Jing], Heisterkamp, D.R., Dai, H.K.,
Adaptive quasiconformal kernel nearest neighbor classification,
PAMI(26), No. 5, May 2004, pp. 656-661.
IEEE Abstract. 0404
BibRef
Earlier:
Adaptive kernel metric nearest neighbor classification,
ICPR02(III: 33-36).
IEEE DOI 0211
BibRef

Martínez Hinarejos, C.D., Juan, A., Casacuberta, F.,
Median strings for k-nearest neighbour classification,
PRL(24), No. 1-3, January 2003, pp. 173-181.
Elsevier DOI 0211
BibRef

d'Haes, W.[Wim], van Dyck, D.[Dirk], Rodet, X.[Xavier],
PCA-based branch and bound search algorithms for computing K nearest neighbors,
PRL(24), No. 9-10, June 2003, pp. 1437-1451.
Elsevier DOI 0304
BibRef

Kudo, M.[Mineichi], Masuyama, N.[Naoto], Toyama, J.[Jun], Shimbo, M.[Masaru],
Simple termination conditions for k-nearest neighbor method,
PRL(24), No. 9-10, June 2003, pp. 1203-1213.
Elsevier DOI 0304
BibRef

Toyama, J.[Jun], Kudo, M.[Mineichi], Imai, H.[Hideyuki],
Probably correct k-nearest neighbor search in high dimensions,
PR(43), No. 4, April 2010, pp. 1361-1372.
Elsevier DOI 1002
Pattern recognition; The k-nearest neighbor method; Probably correct algorithm; PAC framework BibRef

Bressan, M.[Marco], Vitrià, J.[Jordi],
Nonparametric discriminant analysis and nearest neighbor classification,
PRL(24), No. 15, November 2003, pp. 2743-2749.
Elsevier DOI 0308
BibRef

Masip, D.[David], Vitrià, J.[Jordi],
Boosted discriminant projections for nearest neighbor classification,
PR(39), No. 2, February 2006, pp. 164-170.
Elsevier DOI 0512
BibRef

Pujol, O.[Oriol], Masip, D.[David],
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary,
PAMI(31), No. 6, June 2009, pp. 1140-1146.
IEEE DOI 0904
Discriminative learning based on piece-wise linear approximation for non-linear boundaries between clusters. BibRef

Yen, C.W.[Chen-Wen], Young, C.N.[Chieh-Neng], Nagurka, M.L.[Mark L.],
A vector quantization method for nearest neighbor classifier design,
PRL(25), No. 6, 19 April 2004, pp. 725-731.
Elsevier DOI 0405
BibRef

Yang, C.Y.[Chan-Yun], Chou, J.J.[Jui-Jen],
A comparative evaluation approach for the classification of rotifers with modified non-parametric kNN,
IVC(23), No. 4, 1 April 2005, pp. 427-439.
Elsevier DOI 0501
BibRef

Prudent, Y., Ennaji, A.,
A K Nearest Classifier design,
ELCVIA(5), No. 2, 2005, pp. 58-71.
DOI Link 0506
BibRef
And:
A topology based multi-classifier system,
ICDAR05(II: 670-674).
IEEE DOI 0508
BibRef

Lozano, M.[Manuel], Martínez Sotoca, J.[José], Salvador Sánchez, J., Pla, F., Pekalska, E., Duin, R.P.W.,
Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces,
PR(39), No. 10, October 2006, pp. 1827-1838.
Elsevier DOI 0606
Dissimilarity representation; Prototype selection; Adaptive condensing; EM algorithm; Normal density based classifier; Nearest neighbour rule BibRef

Veenman, C.J.[Cor J.], Tax, D.M.J.[David M. J.],
LESS: A Model-Based Classifier for Sparse Subspaces,
PAMI(27), No. 9, September 2005, pp. 1496-1500.
IEEE DOI 0508
BibRef
Earlier:
A Weighted Nearest Mean Classifier for Sparse Subspaces,
CVPR05(II: 1171-1176).
IEEE DOI 0507
BibRef

Veenman, C.J.[Cor J.], Reinders, M.J.T.[Marcel J.T.],
The Nearest Subclass Classifier: A Compromise between the Nearest Mean and Nearest Neighbor Classifier,
PAMI(27), No. 9, September 2005, pp. 1417-1429.
IEEE DOI 0508
BibRef

Ghosh, A.K., Chaudhuri, P., Murthy, C.A.,
On Visualization and Aggregation of Nearest Neighbor Classifiers,
PAMI(27), No. 10, October 2005, pp. 1592-1602.
IEEE DOI 0509
In K-NN classifier, find the optimal K. BibRef

Ghosh, A.K., Chaudhuri, P., Murthy, C.A.,
Multiscale Classification Using Nearest Neighbor Density Estimates,
SMC-B(36), No. 5, October 2006, pp. 1139-1148.
IEEE DOI 0609
BibRef

Wang, J.G.[Ji-Gang], Neskovic, P.[Predrag], Cooper, L.N.[Leon N.],
Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence,
PR(39), No. 3, March 2006, pp. 417-423.
Elsevier DOI 0601
BibRef

Chi, M.M.[Ming-Min], Bruzzone, L.[Lorenzo],
An ensemble-driven k-NN approach to ill-posed classification problems,
PRL(27), No. 4, March 2006, pp. 301-307.
Elsevier DOI 0602
Ill-posed classification problems; Semisupervised classification; Semilabeled samples; Ensemble methods; Automatic classification; Remote sensing BibRef

Bruzzone, L.[Lorenzo], Chi, M., Marconcini, M.,
A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images,
GeoRS(44), No. 11, November 2006, pp. 3363-3373.
IEEE DOI 0611
BibRef

Chi, M., Bruzzone, L.,
Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal,
GeoRS(45), No. 6, June 2007, pp. 1870-1880.
IEEE DOI 0706
BibRef

Inamdar, S., Bovolo, F., Bruzzone, L.[Lorenzo], Chaudhuri, S.,
Multidimensional Probability Density Function Matching for Preprocessing of Multitemporal Remote Sensing Images,
GeoRS(46), No. 4, April 2008, pp. 1243-1252.
IEEE DOI 0803
BibRef

Yang, C.[Chen], Bruzzone, L.[Lorenzo], Sun, F., Lu, L.J.[Lai-Jun], Guan, R.C.[Ren-Chu], Liang, Y.C.,
A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images,
GeoRS(48), No. 6, June 2010, pp. 2647-2659.
IEEE DOI 1006

See also Fuzzy-Statistics-Based Principal Component Analysis (FS-PCA) Method for Multispectral Image Enhancement and Display, A. BibRef

Yang, C.[Chen], Bruzzone, L.[Lorenzo], Guan, R.C.[Ren-Chu], Lu, L.J.[Lai-Jun], Liang, Y.C.,
Incremental and Decremental Affinity Propagation for Semisupervised Clustering in Multispectral Images,
GeoRS(51), No. 3, March 2013, pp. 1666-1679.
IEEE DOI 1303
BibRef

Yang, C.[Chen], Tan, Y.L.[Yu-Lei], Bruzzone, L.[Lorenzo], Lu, L.J.[Lai-Jun], Guan, R.C.[Ren-Chu],
Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Yang, C.[Chen], Bruzzone, L.[Lorenzo], Zhao, H., Tan, Y.[Yulei], Guan, R.C.[Ren-Chu],
Superpixel-Based Unsupervised Band Selection for Classification of Hyperspectral Images,
GeoRS(56), No. 12, December 2018, pp. 7230-7245.
IEEE DOI 1812
Hyperspectral imaging, Clustering algorithms, Image segmentation, Redundancy, Correlation, Dimensionality reduction, superpixel BibRef

Zhao, H.[Haishi], Bruzzone, L.[Lorenzo], Guan, R.[Renchu], Zhou, F.F.[Feng-Feng], Yang, C.[Chen],
Spectral-Spatial Genetic Algorithm-Based Unsupervised Band Selection for Hyperspectral Image Classification,
GeoRS(59), No. 11, November 2021, pp. 9616-9632.
IEEE DOI 2111
Genetic algorithms, Biological cells, Hyperspectral imaging, Redundancy, Task analysis, Optimization, Genetics, remote sensing superpixels BibRef

Muezzinoglu, M.K.[Mehmet K.], Zurada, J.M.[Jacek M.],
RBF-based neurodynamic nearest neighbor classification in real pattern space,
PR(39), No. 5, May 2006, pp. 747-760.
Elsevier DOI Neurodynamics; Associative memory; Radial basis functions 0604
BibRef

Wang, H.[Hui],
Nearest Neighbors by Neighborhood Counting,
PAMI(28), No. 6, June 2006, pp. 942-953.
IEEE DOI 0605
BibRef

Wang, H.[Hui],
Neighborhood Counting Measure and Minimum Risk Metric,
PAMI(32), No. 4, April 2010, pp. 766-768.
IEEE DOI 1003
NCM based on counting all common neighborhoods in a data space. Address comments in:
See also About Neighborhood Counting Measure Metric and Minimum Risk Metric. BibRef

Argentini, A.[Andrea], Blanzieri, E.[Enrico],
About Neighborhood Counting Measure Metric and Minimum Risk Metric,
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Cervantes, A., Galvan, I.M., Isasi, P.,
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Shibata, T.[Tomoyuki], Yamaguchi, O.[Osamu],
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Differential evolution; Prototype generation; Prototype selection; Evolutionary algorithms; Classification BibRef

Triguero, I.[Isaac], Derrac, J., Garcia, S.[Salvador], Herrera, F.[Francisco],
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Garcia, S.[Salvador], Derrac, J.[Joaquin], Cano, J.[Jose], Herrera, F.[Francisco],
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Derrac, J.[Joaquin], Triguero, I.[Isaac], Garcia, S.[Salvador], Herrera, F.[Francisco],
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Wang, Z.X.[Zheng-Xiang], Hu, Y.Q.[Yi-Qun], Chia, L.T.[Liang-Tien],
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Image-to-class distance; Distance learning; Image classification; Nearest-neighbor classification BibRef

Uchida, Y.[Yusuke], Takagi, K.[Koichi], Kawada, R.[Ryoichi],
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Neo, T.K.C.[Toh Koon Charlie], Ventura, D.[Dan],
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ICPR14(1478-1483)
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Song, Q.B.[Qin-Bao], Wang, G.T.[Guang-Tao], Wang, C.[Chao],
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Sáez, J.A.[José A.], Derrac, J.[Joaquín], Luengo, J.[Julián], Herrera, F.[Francisco],
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Ritter, G.X.[Gerhard X.], Nieves-Vázquez, J.A.[José-A.], Urcid, G.[Gonzalo],
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Calvo-Zaragoza, J.[Jorge], Valero-Mas, J.J.[Jose J.], Rico-Juan, J.R.[Juan R.],
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Prototype Generation on Structural Data Using Dissimilarity Space Representation: A Case of Study,
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Valero-Mas, J.J.[Jose J.], Calvo-Zaragoza, J.[Jorge], Rico-Juan, J.R.[Juan R.], Iñesta, J.M.[José M.],
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Bourrier, A.[Anthony], Perronnin, F.[Florent], Gribonval, R.[Rémi], Pérez, P.[Patrick], Jégou, H.[Hervé],
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Xiao, C., Chaovalitwongse, W.A.,
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Liu, X.L.[Xiang-Long], Deng, C.[Cheng], Lang, B.[Bo], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
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Liu, X.L.[Xiang-Long], Huang, L., Deng, C.[Cheng], Lang, B., Tao, D.C.[Da-Cheng],
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Liu, X.L.[Xiang-Long], Li, Z.J.[Zhu-Jin], Deng, C.[Cheng], Tao, D.C.[Da-Cheng],
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Liu, X.L.[Xiang-Long], Huang, L., Deng, C.[Cheng], Lu, J., Lang, B.,
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Datta, S.[Shounak], Misra, D.[Debaleena], Das, S.[Swagatam],
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Gallego, A.J.[Antonio-Javier], Calvo-Zaragoza, J.[Jorge], Valero-Mas, J.J.[Jose J.], Rico-Juan, J.R.[Juan R.],
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Myhre, J.N.[Jonas Nordhaug], Mikalsen, K.Ø.[Karl Øyvind], Løkse, S.[Sigurd], Jenssen, R.[Robert],
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Consensus Clustering Using kNN Mode Seeking,
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Shi, B.[Bing], Han, L.X.[Li-Xin], Yan, H.[Hong],
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López, J.[Julio], Maldonado, S.[Sebastián],
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PRL(110), 2018, pp. 36-43.
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Chen, S.B.[Si-Bao], Xu, Y.L.[Yu-Lan], Ding, C.H.Q.[Chris H.Q.], Luo, B.[Bin],
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Sparse minimization, Nonnegative, Locally linear KNN, Robustness, Image recognition BibRef

Zhang, Y.Q.[You-Qiang], Cao, G.[Guo], Wang, B.S.[Bi-Sheng], Li, X.S.[Xue-Song],
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IEEE DOI 1903
Hashing. Binary codes, Tensile stress, Optimization, Measurement, Quantization (signal), Manifolds, Encoding, Binary code learning, discrete optimization BibRef

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Binary embedding, Cross-modal retrieval, Inverted indexing, Learning to rank, Nearest neighbor search BibRef

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Vector quantization, nearest neighbour search, image retrieval, distributed optimization BibRef

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Machine learning, Multi-dimensional classification, Feature augmentation, Class dependencies BibRef

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Clustering, Mutual neighbors, Dimensionality reduction, Arbitrary shapes, Nearest neighbors, Density peak BibRef

Cariou, C.[Claude], Le Moan, S.[Steven], Chehdi, K.[Kacem],
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Quicker ADC: Unlocking the Hidden Potential of Product Quantization With SIMD,
PAMI(43), No. 5, May 2021, pp. 1666-1677.
IEEE DOI 2104
Quantization (signal), Filtering, Binary codes, Throughput, Indexes, Open source software, Stress, Image databases, SIMD BibRef

Su, H.J.[Hong-Jun], Yu, Y.[Yao], Wu, Z.Y.[Zhao-Yue], Du, Q.[Qian],
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Training, Testing, Collaboration, Dictionaries, Hyperspectral imaging, Collaborative representation, shape-adaptive (SA) neighborhood BibRef

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Generalized Large Margin kNN for Partial Label Learning,
MultMed(24), 2022, pp. 1055-1066.
IEEE DOI 2203
Phase locked loops, Noise measurement, Optimization, Measurement, Training, Faces, Data structures, Partial label, classification, metric learning BibRef

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A Distributed Hybrid Indexing for Continuous KNN Query Processing over Moving Objects,
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Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification,
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Clustering, In-tree, Hierarchical nearest neighbor descent, Mass cytometry BibRef

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D-NND: A Hierarchical Density Clustering Method via Nearest Neighbor Descent,
ICPR18(1414-1419)
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Deep Nearest Neighbor Descent. Artificial neural networks, Estimation, Clustering methods, Kernel, Feature extraction, Unsupervised learning, Bandwidth BibRef

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Mutual K-nearest neighbors, Adaptive clustering, Maximizing mutual information BibRef

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Features subset, Nearest Neighbours Rule, NN Ensemble, Classification BibRef

Tobin, J.[Joshua], Zhang, M.[Mimi],
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Density-based clustering, nearest-neighbor graph, density peaks, semi-supervised clustering, multi-image matching BibRef

Chen, J.[Jiang], Zhang, X.Y.[Xian-Yong], Yuan, Z.[Zhong],
Feature selections based on two-type overlap degrees and three-view granulation measures for k-nearest-neighbor rough sets,
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K-nearest-neighbor rough set, Neighborhood rough set, Feature selection, Overlap degree, Dependency degree, Information measure BibRef

Ai, L.[Liefu], Jiang, C.[Changyu],
Learning Adaptive Hypersphere: Boosting Efficiency on Approximate Nearest Neighbor Search,
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IEEE DOI 2409
Vectors, Training, Accuracy, Filtration, Convergence, Computational modeling, Search methods, vector quantization BibRef

Xie, J.[Jiang], Xiang, X.X.[Xue-Xin], Xia, S.[Shuyin], Jiang, L.[Lian], Wang, G.[Guoyin], Gao, X.B.[Xin-Bo],
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Computational modeling, Data models, Clustering methods, Machine learning, Clustering algorithms, neighbor relationship BibRef

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Encoding, Quantization (signal), Binary codes, Clustering algorithms, Vectors, Indexes, Search problems, quantization BibRef

Nakamura, K.[Kazumoto], Nozawa, Y.[Yuji], Lin, Y.C.[Yu-Chieh], Nakata, K.[Kengo], Ng, Y.Y.[You-Yang],
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Zhang, H.[Haokui], Tang, B.[Buzhou], Hu, W.Z.[Wen-Ze], Wang, X.Y.[Xiao-Yu],
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A cheaper Rectified-Nearest-Feature-Line-Segment classifier based on safe points,
ICPR21(2787-2794)
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Training, Interpolation, Extrapolation, Explosions, Computational efficiency BibRef

Ju, X.B.[Xin-Bo], Shao, S.[Shuo], Long, H.[Huan], Wang, W.Z.[Wei-Zhe],
Nearest Neighbor Classification Based on Activation Space of Convolutional Neural Network,
ICPR21(431-437)
IEEE DOI 2105
Neural networks, Tools, Classification algorithms, Convolutional neural networks, Image classification BibRef

Jia, B.B.[Bin-Bin], Zhang, M.L.[Min-Ling],
Md-knn: An Instance-based Approach for Multi-Dimensional Classification,
ICPR21(126-133)
IEEE DOI 2105
Estimation, Machine learning, Benchmark testing BibRef

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Efficient Parameter-Free Clustering Using First Neighbor Relations,
CVPR19(8926-8935).
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He, X.Y.[Xiang-Yu], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
K-Nearest Neighbors Hashing,
CVPR19(2834-2843).
IEEE DOI 2002
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Rattaphun, M., Prayoonwong, A., Chiu, C.Y.,
Indexing in k-Nearest Neighbor Graph by Hash-Based Hill-Climbing,
MVA19(1-4)
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approximation theory, file organisation, graph theory, nearest neighbour methods, query processing, search problems, hashing BibRef

Orozco-Alzate, M.[Mauricio], Baldo, S.[Sisto], Bicego, M.[Manuele],
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A Distributed Shared Nearest Neighbors Clustering Algorithm,
CIARP17(710-718).
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Ezghari, S., Benouini, R., Zahi, A., Zenkouar, K.,
Learning efficient and interpretable prototypes from data for nearest neighbor classification method,
ISCV17(1-7)
IEEE DOI 1710
nearest neighbor classification method, Classification algorithms, clustering, interpretability, nearest neighbor, prototype, learning BibRef

Iwai, Y., Nishiyama, M., Yoshimura, H.,
Asymmetric locality preserving projection and its application to k-nearest neighbor method,
MVA17(55-58)
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Eigenvalues and eigenfunctions, Linear programming, Matrix decomposition, Optimization, Organizations, Principal component analysis, Symmetric matrices BibRef

Aryal, A.M., Wang, S.J.[Su-Jing],
Discovery of patterns in spatio-temporal data using clustering techniques,
ICIVC17(990-995)
IEEE DOI 1708
Clustering algorithms, Noise measurement, Public transportation, Shape, clustering, shared nearest neighbor clustering, spatial-temporal clustering, spatial-temporal, patterns BibRef

Bicego, M., Loog, M.,
Weighted K-Nearest Neighbor revisited,
ICPR16(1642-1647)
IEEE DOI 1705
Degradation, Diversity reception, Terminology, Testing, Training BibRef

Barddal, J.P., Gomes, H.M., Granatyr, J., de Souza Britto, A., Enembreck, F.,
Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning,
ICPR16(2186-2191)
IEEE DOI 1705
Adaptation models, Entropy, Feature extraction, Generators, Light emitting diodes, Proposals BibRef

Ozan, E.C., Kiranyaz, S., Gabbouj, M.,
Joint K-Means quantization for Approximate Nearest Neighbor Search,
ICPR16(3645-3649)
IEEE DOI 1705
Encoding, Hamming distance, Optimization, Search problems, Training, Vector, quantization BibRef

Ozan, E.C., Riabchenko, E., Kiranyaz, S., Gabbouj, M.,
A vector quantization based k-NN approach for large-scale image classification,
IPTA16(1-6)
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image classification BibRef

Woodley, A.[Alan], Chappell, T.[Timothy], Geva, S.[Shlomo], Nayak, R.[Richi],
Efficient Feature Selection and Nearest Neighbour Search for Hyperspectral Image Classification,
DICTA16(1-8)
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Classification algorithms BibRef

Harwood, B., Drummond, T.W.[Tom W.],
FANNG: Fast Approximate Nearest Neighbour Graphs,
CVPR16(5713-5722)
IEEE DOI 1612
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Heo, J.P.[Jae-Pil], Lin, Z.[Zhe], Shen, X.H.[Xiao-Hui], Brandt, J.[Jonathan], Yoon, S.E.[Sung-Eui],
Shortlist Selection with Residual-Aware Distance Estimator for K-Nearest Neighbor Search,
CVPR16(2009-2017)
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Fränti, P.[Pasi], Mariescu-Istodor, R.[Radu], Zhong, C.M.[Cai-Ming],
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SSSPR16(207-217).
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Malach, T., Pomenkova, J.,
Learning of a robusted nearest neighbor classifier using multiple training data,
WSSIP16(1-4)
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closed circuit television BibRef

Wang, Z., Yuan, X.T.[Xiao-Tong], Liu, Q., Yan, S.C.[Shui-Cheng],
Additive Nearest Neighbor Feature Maps,
ICCV15(2866-2874)
IEEE DOI 1602
Additives BibRef

Nguyen, T.A.[Tuan Anh], Matsui, Y.[Yusuke], Yamasaki, T.[Toshihiko], Aizawa, K.[Kiyoharu],
Searching for nearest neighbors with a dense space partitioning,
ICIP15(4461-4465)
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computer vision BibRef

Liu, Q.F.[Qing-Feng], Puthenputhussery, A.[Ajit], Liu, C.J.[Cheng-Jun],
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ICIP15(1810-1814)
IEEE DOI 1512
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Bhattacharya, G.[Gautam], Ghosh, K.[Koushik], Chowdhury, A.S.[Ananda S.],
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PReMI17(21-27).
Springer DOI 1711
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Earlier:
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ICAPR15(1-5)
IEEE DOI 1511
learning (artificial intelligence) BibRef

Reineking, T.[Thomas], Kluth, T.[Tobias], Nakath, D.[David],
Adaptive Information Selection in Images: Efficient Naive Bayes Nearest Neighbor Classification,
CAIP15(I:350-361).
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Saeedan, F.[Faraz], Caputo, B.[Barbara],
Towards Learning Free Naive Bayes Nearest Neighbor-Based Domain Adaptation,
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Alhammami, M.[Muhammad], Ooi, C.P.[Chee Pun], Tan, W.H.[Wooi-Haw],
Violence Recognition Using Harmonic Mean of Distances and Relational Velocity with K-Nearest Neighbour Classifier,
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Li, X.Z.[Xue-Zhen], Kurita, T.,
Nonlinear discriminant analysis using K nearest neighbor estimation,
FCV15(1-6)
IEEE DOI 1506
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ICPR14(4358-4363)
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Park, M.W.[Min-Woo], Gunda, K.[Kiran], Gupta, H.[Himaanshu], Shafique, K.[Khurram],
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IEEE DOI 1409
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Tommasi, T.[Tatiana], Caputo, B.[Barbara],
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ICCV13(897-904)
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Domain Adaptation; Naive Bayes Nearest Neighbor BibRef

Jin, Z.M.[Zhong-Ming], Hu, Y.[Yao], Lin, Y.[Yue], Zhang, D.[Debing], Lin, S.[Shiding], Cai, D.[Deng], Li, X.L.[Xue-Long],
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ICCV13(257-264)
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Approximate Nearest Neighbor Search; Hashing BibRef

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CVPR13(446-451)
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Fast Nearest Neighbor Techniques .