14.1.4.1 Semi-Supervised, Unsupervised Dimensionality Reduction

Chapter Contents (Back)
Dimensionality. Dimensionality Reduction.

de Backer, S., Naud, A., Scheunders, P.,
Nonlinear Dimensionality Reduction Techniques for Unsupervised Feature Extraction,
PRL(19), No. 8, June 1998, pp. 711-720. 9808
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Cappelli, R.[Raffaele], Maio, D.[Dario], Maltoni, D.[Davide],
Multispace KL for Pattern Representation and Classification,
PAMI(23), No. 9, September 2001, pp. 977-996.
IEEE DOI 0110
For unsupervised dimensionality reduction. BibRef

Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
Semi-supervised orthogonal discriminant analysis via label propagation,
PR(42), No. 11, November 2009, pp. 2615-2627.
Elsevier DOI 0907
Subspace learning, Discriminant analysis, Dimensionality reduction; Trace ratio, Semi-supervised learning BibRef

Nie, F.P.[Fei-Ping], Xu, D., Li, X., Xiang, S.M.[Shi-Ming],
Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression,
SMC-B(41), No. 3, June 2011, pp. 675-685.
IEEE DOI 1106
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Xu, D., Yan, X.,
Semi-Supervised Bilinear Subspace Learning,
IP(18), No. 7, July 2009, pp. 1671-1676.
IEEE DOI 0906
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Kim, J.K.[Jong Kyoung], Choi, S.J.[Seung-Jin],
Clustering with r-regular graphs,
PR(42), No. 9, September 2009, pp. 2020-2028.
Elsevier DOI 0905
b-Matching, Cluster utility, Graph-based clustering, Regular graphs BibRef

Lee, S.H.[Seung-Hak], Choi, S.J.[Seung-Jin],
Landmark MDS ensemble,
PR(42), No. 9, September 2009, pp. 2045-2053.
Elsevier DOI 0905
Dimensionality reduction, Embedding, Multidimensional scaling (MDS); Unsupervised learning BibRef

Hou, C.P.[Chen-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.[Yi], Nie, F.P.[Fei-Ping],
Multiple view semi-supervised dimensionality reduction,
PR(43), No. 3, March 2010, pp. 720-730.
Elsevier DOI 1001
Dimensionality reduction, Semi-supervised, Multiple view, Domain knowledge BibRef

Nie, F.P.[Fei-Ping], Xu, D.[Dong], Tsang, I.W.H., Zhang, C.S.[Chang-Shui],
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction,
IP(19), No. 7, July 2010, pp. 1921-1932.
IEEE DOI 1007
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Faivishevsky, L.[Lev], Goldberger, J.[Jacob],
An unsupervised data projection that preserves the cluster structure,
PRL(33), No. 3, 1 February 2012, pp. 256-262.
Elsevier DOI 1201
Unsupervised dimensionality reduction, Mutual information, Clustering BibRef

Gu, N.N.[Nan-Nan], Fan, M.Y.[Ming-Yu], Qiao, H.[Hong], Zhang, B.[Bo],
Discriminative Sparsity Preserving Projections for Semi-Supervised Dimensionality Reduction,
SPLetters(19), No. 7, July 2012, pp. 391-394.
IEEE DOI 1206
BibRef

Zhao, M.B.[Ming-Bo], Zhang, Z.[Zhao], Chow, T.W.S.[Tommy W.S.],
Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction,
PR(45), No. 4, 2012, pp. 1482-1499.
Elsevier DOI 1410
Dimensionality reduction BibRef

Mu, T.T.[Ting-Ting], Goulermas, J.Y.[John Yannis], Tsujii, J.[Jun'ichi], Ananiadou, S.[Sophia],
Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data,
PAMI(34), No. 11, November 2012, pp. 2216-2232.
IEEE DOI 1209
Supervised and semi-supervised dimensionality reduction. BibRef

Han, Y.H.[Ya-Hong], Wu, F.[Fei], Tao, D.C.[Da-Cheng], Shao, J.[Jian], Zhuang, Y.T.[Yue-Ting], Jiang, J.M.[Jian-Min],
Sparse Unsupervised Dimensionality Reduction for Multiple View Data,
CirSysVideo(22), No. 10, October 2012, pp. 1485-1496.
IEEE DOI 1210
BibRef

Jukic, A.[Ante], Filipovic, M.[Marko],
Supervised feature extraction for tensor objects based on maximization of mutual information,
PRL(34), No. 13, 2013, pp. 1476-1484.
Elsevier DOI 1308
Dimensionality reduction BibRef

Zhang, Z.[Zhao], Yan, S.C.[Shui-Cheng], Zhao, M.B.[Ming-Bo],
Pairwise Sparsity Preserving Embedding for Unsupervised Subspace Learning and Classification,
IP(22), No. 12, 2013, pp. 4640-4651.
IEEE DOI 1312
convex programming BibRef

Yang, S., Jin, P., Li, B., Yang, L., Xu, W., Jiao, L.,
Semisupervised Dual-Geometric Subspace Projection for Dimensionality Reduction of Hyperspectral Image Data,
GeoRS(52), No. 6, June 2014, pp. 3587-3593.
IEEE DOI 1403
Accuracy BibRef

Kim, K.[Kyoungok], Lee, J.W.[Jae-Wook],
Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction,
PR(47), No. 2, 2014, pp. 758-768.
Elsevier DOI 1311
Text visualization BibRef

Shen, X.B.[Xiao-Bo], Sun, Q.S.[Quan-Sen],
A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction,
JVCIR(25), No. 8, 2014, pp. 1894-1904.
Elsevier DOI 1411
Canonical correlation analysis BibRef

Wang, F.[Fang], Li, R.[Renfu], Lei, Z.K.[Zhi-Kun], Ni, X.S.[Xuelei Sherry], Huo, X.M.[Xiao-Ming], Chen, M.[Ming],
Kernel fusion-refinement for semi-supervised nonlinear dimension reduction,
PRL(63), No. 1, 2015, pp. 16-22.
Elsevier DOI 1508
Kernel fusion-refinement BibRef

Guo, X., Tie, Y., Qi, L., Guan, L.,
A Novel Semi-Supervised Dimensionality Reduction Framework,
MultMedMag(23), No. 2, April 2016, pp. 28-41.
IEEE DOI 1605
Algorithm design and analysis BibRef

Wang, S.[Sheng], Lu, J.F.[Jian-Feng], Gu, X.J.[Xing-Jian], Du, H.S.[Hai-Shun], Yang, J.Y.[Jing-Yu],
Semi-supervised linear discriminant analysis for dimension reduction and classification,
PR(57), No. 1, 2016, pp. 179-189.
Elsevier DOI 1605
Dimension reduction BibRef

Yu, T., Zhang, W.,
Semisupervised Multilabel Learning With Joint Dimensionality Reduction,
SPLetters(23), No. 6, June 2016, pp. 795-799.
IEEE DOI 1606
computational complexity BibRef

Song, Y.Q.[Yang-Qiu], Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui],
Semi-supervised sub-manifold discriminant analysis,
PRL(29), No. 13, 1 October 2008, pp. 1806-1813.
Elsevier DOI 0804
Semi-supervised learning; Dimensionality reduction; Sub-manifold discriminative embedding BibRef

Song, Y.Q.[Yang-Qiu], Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui], Xiang, S.M.[Shi-Ming],
A unified framework for semi-supervised dimensionality reduction,
PR(41), No. 9, September 2008, pp. 2789-2799.
Elsevier DOI 0806
Dimensionality reduction; Discriminant analysis, Manifold analysis; Semi-supervised learning BibRef

Zhang, Q.[Qin], Sun, J.Y.[Jian-Yuan], Zhong, G.Q.[Guo-Qiang], Dong, J.Y.[Jun-Yu],
Random Multi-Graphs: A semi-supervised learning framework for classification of high dimensional data,
IVC(60), No. 1, 2017, pp. 30-37.
Elsevier DOI 1704
Semi-supervised learning BibRef

Sakarya, U.[Ufuk],
Dimension reduction using global and local pattern information-based maximum margin criterion,
SIViP(10), No. 5, May 2016, pp. 903-909.
WWW Link. 1608
BibRef

Sakarya, U.[Ufuk],
Semi-supervised dimension reduction approaches integrating global and local pattern information,
SIViP(13), No. 1, February 2019, pp. 171-178.
WWW Link. 1901
BibRef

Yu, M.Y.[Meng-Yang], Shao, L.[Ling], Zhen, X.T.[Xian-Tong], He, X.F.[Xiao-Fei],
Local Feature Discriminant Projection,
PAMI(38), No. 9, September 2016, pp. 1908-1914.
IEEE DOI 1609
supervised dimensionality reduction of local features. feature extraction BibRef

Chen, P.[Puhua], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Zhao, J.Q.[Jia-Qi], Zhao, Z.Q.[Zhi-Qiang], Liu, S.[Shuai],
Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction,
PR(61), No. 1, 2017, pp. 361-378.
Elsevier DOI 1705
Semi-supervised learning BibRef

Sun, L.[Lu], Kudo, M.[Mineichi], Kimura, K.[Keigo],
READER: Robust Semi-Supervised Multi-Label Dimension Reduction,
IEICE(E100-D), No. 10, October 2017, pp. 2597-2604.
WWW Link. 1710
BibRef

Zhang, J., Yu, J., Tao, D.,
Local Deep-Feature Alignment for Unsupervised Dimension Reduction,
IP(27), No. 5, May 2018, pp. 2420-2432.
IEEE DOI 1804
data visualisation, feature extraction, image representation, pattern classification, unsupervised learning, LDFA method, locality preserving BibRef

Mikalsen, K..[Karl yvind], Soguero-Ruiz, C.[Cristina], Bianchi, F.M.[Filippo Maria], Jenssen, R.[Robert],
Noisy multi-label semi-supervised dimensionality reduction,
PR(90), 2019, pp. 257-270.
Elsevier DOI 1903
Label noise, Multi-label learning, Semi-supervised learning, Dimensionality reduction BibRef

Murphy, J.M.[James M.],
Spatially regularized active diffusion learning for high-dimensional images,
PRL(135), 2020, pp. 213-220.
Elsevier DOI 2006
Machine learning, Semi-supervised learning, Active learning, Diffusion geometry, Manifold learning BibRef

Lu, Y.D.[Ying-Di], Wu, G.[Gang],
Fast and incremental algorithms for exponential semi-supervised discriminant embedding,
PR(108), 2020, pp. 107530.
Elsevier DOI 2008
Semi-supervised discriminant embedding (SDE), Local discriminant embedding (LDE), Dimensionality reduction BibRef

Pang, W.R.[Wen-Rao], Wu, G.[Gang],
Fast algorithms for incremental and decremental semi-supervised discriminant analysis,
PR(131), 2022, pp. 108888.
Elsevier DOI 2208
Dimensionality reduction, Semi-supervised discriminant analysis, Incremental learning, Modified total scatter matrix BibRef

Chen, H.[Hong], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
Adaptive Flexible Optimal Graph for Unsupervised Dimensionality Reduction,
SPLetters(28), 2021, pp. 2162-2166.
IEEE DOI 2112
Principal component analysis, Manifolds, Manifold learning, Task analysis, Resource management, Optics, Entropy, unsupervised learning BibRef

Xie, L.P.[Li-Ping], Guo, W.[Weili], Wei, H.[Haikun], Tang, Y.Y.[Yuan-Yan], Tao, D.C.[Da-Cheng],
Efficient Unsupervised Dimension Reduction for Streaming Multiview Data,
Cyber(52), No. 3, March 2022, pp. 1772-1784.
IEEE DOI 2203
Dimensionality reduction, Correlation, Memory management, Prediction algorithms, Optimization, Training, Data models, unsupervised multiview dimension reduction (UMDR) BibRef

Yuan, Z.[Zhong], Chen, H.M.[Hong-Mei], Li, T.R.[Tian-Rui],
Exploring interactive attribute reduction via fuzzy complementary entropy for unlabeled mixed data,
PR(127), 2022, pp. 108651.
Elsevier DOI 2205
Fuzzy rough set theory, Unsupervised attribute reduction, Complementary entropy, Maximal information, Mixed data BibRef

Lu, X.H.[Xiao-Huan], Long, J.[Jiang], Wen, J.[Jie], Fei, L.[Lunke], Zhang, B.[Bob], Xu, Y.[Yong],
Locality preserving projection with symmetric graph embedding for unsupervised dimensionality reduction,
PR(131), 2022, pp. 108844.
Elsevier DOI 2208
Dimensionality reduction, Feature extraction, Graph embedding, Unsupervised learning BibRef

Wang, J.[Jikui], Wu, Y.[Yiwen], Li, B.[Bing], Yang, Z.G.[Zhen-Guo], Nie, F.P.[Fei-Ping],
Fast anchor graph preserving projections,
PR(146), 2024, pp. 109996.
Elsevier DOI Code:
WWW Link. 2311
Dimensionality reduction, Principal component analysis, Anchor graph, Unsupervised learning BibRef

Wang, Q.[Quan], Wang, F.[Fei], Li, Z.H.[Zhong-Heng], Wang, Z.[Zheng], Nie, F.P.[Fei-Ping],
Coordinate Descent Optimized Trace Difference Model for Joint Clustering and Feature Extraction,
PR(146), 2024, pp. 110062.
Elsevier DOI 2311
Clustering, Coordinate descent method, Feature extraction, Trace difference criterion, Unsupervised learning BibRef


Spathis, D.[Dimitris], Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Fast, Visual and Interactive Semi-supervised Dimensionality Reduction,
CEFR-LCV18(IV:550-563).
Springer DOI 1905
BibRef

Yang, Q.W.[Qian-Wen], Sun, F.C.[Fu-Chun],
Unsupervised Local Linear Preserving Manifold Reduction with Uncertainty Pretraining for Image Recognition,
CVS17(527-539).
Springer DOI 1711
Manifold learning. BibRef

Wang, Y.M.[Ya-Ming], Morariu, V.I.[Vlad I.], Davis, L.S.[Larry S.],
Unsupervised Feature Extraction Inspired by Latent Low-Rank Representation,
WACV15(542-549)
IEEE DOI 1503
Algorithm design and analysis BibRef

Morariu, V.I.[Vlad I.], Ahmed, E.[Ejaz], Santhanam, V.[Venkataraman], Harwood, D.[David], Davis, L.S.[Larry S.],
Composite Discriminant Factor analysis,
WACV14(564-571)
IEEE DOI 1406
Accuracy BibRef

Carreira-Perpinan, M.A.[Miguel A.], Lu, Z.D.[Zheng-Dong],
Parametric dimensionality reduction by unsupervised regression,
CVPR10(1895-1902).
IEEE DOI 1006
BibRef
Earlier:
Dimensionality reduction by unsupervised regression,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Yang, W.[Wuyi], Zhang, S.W.[Shu-Wu], Liang, W.[Wei],
A Graph Based Subspace Semi-supervised Learning Framework for Dimensionality Reduction,
ECCV08(II: 664-677).
Springer DOI 0810
BibRef

Gong, H.F.[Hai-Feng], Pan, C.H.[Chun-Hong], Yang, Q.[Qing], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Neural Network Modeling of Spectral Embedding,
BMVC06(I:227).
PDF File. 0609
BibRef
Earlier:
A Semi-Supervised Framework for Mapping Data to the Intrinsic Manifold,
ICCV05(I: 98-105).
IEEE DOI 0510
Reduce dimensionality, but to the intrinsic form. BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Riemannian Manifold Learning, Clustering .


Last update:May 6, 2024 at 15:50:14