14.1.3.5 Ranking

Chapter Contents (Back)
Feature Ranking. Ranking.

Sima, C.[Chao], Dougherty, E.R.[Edward R.],
Optimal convex error estimators for classification,
PR(39), No. 9, September 2006, pp. 1763-1780.
Elsevier DOI
WWW Link. 0606
Bootstrap, Cross-validation, Error estimation; Feature-set ranking, Optimal estimation, Resubstitution, BibRef

Wei, H.L.[Hua-Liang], Billings, S.A.,
Feature Subset Selection and Ranking for Data Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 162-166.
IEEE DOI 0701
Forward Orthogonal Search. Select features 1 at a time. BibRef

Liang, J.N.[Jian-Ning], Yang, S.[Su], Winstanley, A.[Adam],
Invariant optimal feature selection: A distance discriminant and feature ranking based solution,
PR(41), No. 5, May 2008, pp. 1429-1439.
Elsevier DOI 0711
Optimal feature selection, Distance discriminant, Feature ranking BibRef

Yang, S.[Su], Liang, J.N.[Jian-Ning], Wang, Y.Y.[Yuan-Yuan], Winstanley, A.[Adam],
Feature Selection Based on Run Covering,
PSIVT06(208-217).
Springer DOI 0612
BibRef

Hong, Y.[Yi], Kwong, S.[Sam], Chang, Y.C.[Yu-Chou], Ren, Q.S.[Qing-Sheng],
Consensus unsupervised feature ranking from multiple views,
PRL(29), No. 5, 1 April 2008, pp. 595-602.
Elsevier DOI 0802
Clustering, Feature ranking ensembles, Unsupervised feature selection BibRef

Uematsu, K., Lee, Y.,
Statistical Optimality in Multipartite Ranking and Ordinal Regression,
PAMI(37), No. 5, May 2015, pp. 1080-1094.
IEEE DOI 1504
Measurement BibRef

Bellal, F.[Fazia], Elghazel, H.[Haytham], Aussem, A.[Alex],
A semi-supervised feature ranking method with ensemble learning,
PRL(33), No. 10, 15 July 2012, pp. 1426-1433.
Elsevier DOI 1205
Semi-supervised learning, Feature selection, Ensemble learning BibRef

Hernandez-Leal, P.[Pablo], Carrasco-Ochoa, J.A.[J. Ariel], Martínez-Trinidad, J.F.[José Francisco], Olvera-Lopez, J.A.[J. Arturo],
InstanceRank based on borders for instance selection,
PR(46), No. 1, January 2013, pp. 365-375.
Elsevier DOI 1209
Instance selection, Instance ranking, Border instances, Supervised classification BibRef

Olvera-López, J.A.[J. Arturo], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
Mixed Data Object Selection Based on Clustering and Border Objects,
CIARP07(674-683).
Springer DOI 0711
Instance selection. BibRef

Hernandez-Rodriguez, S.[Selene], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
On the selection of base prototypes for LAESA and TLAESA classifiers,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jiang, Y.G.[Yu-Gang], Wang, J.[Jun], Xue, X., Chang, S.F.[Shih-Fu],
Query-Adaptive Image Search With Hash Codes,
MultMed(15), No. 2, 2013, pp. 442-453.
IEEE DOI 1302
BibRef

Jiang, Y.G.[Yu-Gang], Wang, J.[Jun], Chang, S.F.[Shih-Fu],
Lost in binarization: query-adaptive ranking for similar image search with compact codes,
ICMR11(16).
DOI Link 1301
BibRef
And: A2, A1, A3:
Label diagnosis through self tuning for web image search,
CVPR09(1390-1397).
IEEE DOI 0906
Are the initial label good? BibRef

Cánovas-García, F.[Fulgencio], Alonso-Sarría, F.[Francisco],
Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery,
RS(7), No. 4, 2015, pp. 4651-4677.
DOI Link 1505
BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Feature selection for multi-label classification using multivariate mutual information,
PRL(34), No. 3, 1 February 2013, pp. 349-357.
Elsevier DOI 1301
Multi-label feature selection, Multivariate feature selection; Multivariate mutual information, Label dependency BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
SCLS: Multi-label feature selection based on scalable criterion for large label set,
PR(66), No. 1, 2017, pp. 342-352.
Elsevier DOI 1704
Machine learning BibRef

Lim, H.K.[Hyun-Ki], Kim, D.W.[Dae-Won],
Convex optimization approach for multi-label feature selection based on mutual information,
ICPR16(1512-1517)
IEEE DOI 1705
Convex functions, Entropy, Linear programming, Mutual information, Optimization, Redundancy, Time, complexity BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation,
IEICE(E99-D), No. 5, May 2016, pp. 1396-1399.
WWW Link. 1605
BibRef

Lim, H.K.[Hyun-Ki],
Low-rank learning for feature selection in multi-label classification,
PRL(172), 2023, pp. 106-112.
Elsevier DOI 2309
Multi-label classification, Feature selection, Low-rank learning BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Optimization approach for feature selection in multi-label classification,
PRL(89), No. 1, 2017, pp. 25-30.
Elsevier DOI 1704
Multi-label feature selection BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Fast multi-label feature selection based on information-theoretic feature ranking,
PR(48), No. 9, 2015, pp. 2761-2771.
Elsevier DOI 1506
Multi-label feature selection BibRef

Senawi, A.[Azlyna], Wei, H.L.[Hua-Liang], Billings, S.A.[Stephen A.],
A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking,
PR(67), No. 1, 2017, pp. 47-61.
Elsevier DOI 1704
Dimensionality reduction BibRef

Ji, Z., Cui, B., Li, H., Jiang, Y., Xiang, T., Hospedales, T.M.[Timothy M.], Fu, Y.,
Deep Ranking for Image Zero-Shot Multi-Label Classification,
IP(29), 2020, pp. 6549-6560.
IEEE DOI 2007
Testing, Training, Predictive models, Semantics, Correlation, Visualization, Training data, Multi-label classification, transductive learning BibRef

Chen, Z.M.[Zhao-Min], Cui, Q.[Quan], Wei, X.S.[Xiu-Shen], Jin, X.[Xin], Guo, Y.[Yanwen],
Disentangling, Embedding and Ranking Label Cues for Multi-Label Image Recognition,
MultMed(23), 2021, pp. 1827-1840.
IEEE DOI 2107
Correlation, Image recognition, Streaming media, Recurrent neural networks, Task analysis, Computational modeling, ranking BibRef

Viola, R.[Rémi], Gautheron, L.[Léo], Habrard, A.[Amaury], Sebban, M.[Marc],
MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data,
PRL(161), 2022, pp. 161-167.
Elsevier DOI 2209
Imbalanced learning, Tree-based ranking, Average precision, Interpretability BibRef

Fu, Z.[Zheren], Mao, Z.D.[Zhen-Dong], Yan, C.G.[Cheng-Gang], Liu, A.A.[An-An], Xie, H.T.[Hong-Tao], Zhang, Y.D.[Yong-Dong],
Self-Supervised Synthesis Ranking for Deep Metric Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4736-4750.
IEEE DOI 2207
Measurement, Semantics, Transforms, Training, Task analysis, Coordinate measuring machines, Manifolds, Deep metric learning, generative model BibRef

Geng, X.[Xin], Zheng, R.Y.[Ren-Yi], Lv, J.Q.[Jia-Qi], Zhang, Y.[Yu],
Multilabel Ranking with Inconsistent Rankers,
PAMI(44), No. 9, September 2022, pp. 5211-5224.
IEEE DOI 2208
Training, Predictive models, Adaptation models, Task analysis, Machine learning, Machine learning algorithms, Encoding BibRef

Geng, X.[Xin], Luo, L.[Longrun],
Multilabel Ranking with Inconsistent Rankers,
CVPR14(3742-3747)
IEEE DOI 1409
BibRef

Helm, H.S.[Hayden S.], Basu, A.[Amitabh], Athreya, A.[Avanti], Park, Y.[Youngser], Vogelstein, J.T.[Joshua T.], Priebe, C.E.[Carey E.], Winding, M.[Michael], Zlatic, M.[Marta], Cardona, A.[Albert], Bourke, P.[Patrick], Larson, J.[Jonathan], Abdin, M.[Marah], Choudhury, P.[Piali], Yang, W.W.[Wei-Wei], White, C.W.[Christopher W.],
Distance-based positive and unlabeled learning for ranking,
PR(134), 2023, pp. 109085.
Elsevier DOI 2212
Positive-and-unlabeled learning, ranking, network analysis BibRef

Fu, Y.Q.[Yu-Qian], Xie, Y.[Yu], Fu, Y.W.[Yan-Wei], Jiang, Y.G.[Yu-Gang],
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning,
CVPR23(24575-24584)
IEEE DOI 2309
BibRef

Fu, Y.Q.[Yu-Qian], Fu, Y.W.[Yan-Wei], Chen, J.J.[Jing-Jing], Jiang, Y.G.[Yu-Gang],
Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data,
IP(31), 2022, pp. 7078-7090.
IEEE DOI 2212
Feature extraction, Task analysis, Training, Data models, Data mining, Benchmark testing, Visualization, contrastive learning BibRef

Xu, C.M.[Cheng-Ming], Fu, Y.W.[Yan-Wei], Liu, C.[Chen], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue], Zhang, L.[Li], Xue, X.Y.[Xiang-Yang],
Learning Dynamic Alignment via Meta-filter for Few-shot Learning,
CVPR21(5178-5187)
IEEE DOI 2111
Visualization, Adaptation models, Semantics, Benchmark testing, Ordinary differential equations, Information filters BibRef

Li, P.[Pan], Gong, S.G.[Shao-Gang], Wang, C.J.[Cheng-Jie], Fu, Y.W.[Yan-Wei],
Ranking Distance Calibration for Cross-Domain Few-Shot Learning,
CVPR22(9089-9098)
IEEE DOI 2210
Training, Image retrieval, Encoding, Calibration, Pattern recognition, Task analysis, Representation learning BibRef


Liu, C.[Chang], Yu, H.[Han], Li, B.Y.[Bo-Yang], Shen, Z.Q.[Zhi-Qi], Gao, Z.N.[Zhan-Ning], Ren, P.R.[Pei-Ran], Xie, X.[Xuansong], Cui, L.[Lizhen], Miao, C.Y.[Chun-Yan],
Noise-resistant Deep Metric Learning with Ranking-based Instance Selection,
CVPR21(6807-6816)
IEEE DOI 2111
Training, Deep learning, Neural networks, Memory management, Probabilistic logic, Feature extraction, Robustness BibRef

Sun, X.X.[Xiao-Xiao], Hou, Y.Z.[Yun-Zhong], Deng, W.J.[Wei-Jian], Li, H.D.[Hong-Dong], Zheng, L.[Liang],
Ranking Models in Unlabeled New Environments,
ICCV21(11741-11751)
IEEE DOI 2203
Measurement, Codes, Annotations, Computational modeling, Search problems, Task analysis, Image and video retrieval, Datasets and evaluation BibRef

Li, Y.D.[Yan-Dong], Jia, X.[Xuhui], Sang, R.X.[Ruo-Xin], Zhu, Y.K.[Yu-Kun], Green, B.[Bradley], Wang, L.Q.[Li-Qiang], Gong, B.Q.[Bo-Qing],
Ranking Neural Checkpoints,
CVPR21(2662-2672)
IEEE DOI 2111
To use in transfer learning. Deep learning, Training, Network topology, Transfer learning, Benchmark testing, Feature extraction, Topology BibRef

Vargas-Ruíz, L.[Lauro], Franco-Arcega, A.[Anilu], Alonso-Lavernia, M.[María_de_los_Ángeles],
A Novel Criterion to Obtain the Best Feature Subset from Filter Ranking Methods,
MCPR18(12-22).
Springer DOI 1807
BibRef

Li, Y., Song, Y., Luo, J.,
Improving Pairwise Ranking for Multi-label Image Classification,
CVPR17(1837-1845)
IEEE DOI 1711
Adaptation models, Fasteners, Neural networks, Visualization BibRef

Yao, Y., Xin, X., Guo, P.,
A rank minimization-based late fusion method for multi-label image annotation,
ICPR16(847-852)
IEEE DOI 1705
Matrix decomposition, Minimization, Optimization, Predictive models, Sparse matrices, Training BibRef

Kanehira, A., Harada, T.,
Multi-label Ranking from Positive and Unlabeled Data,
CVPR16(5138-5146)
IEEE DOI 1612
BibRef

Cruz, R.[Ricardo], Fernandes, K.[Kelwin], Pinto Costa, J.F.[Joaquim F.], Ortiz, M.P.[María Pérez], Cardoso, J.S.[Jaime S.],
Ordinal Class Imbalance with Ranking,
IbPRIA17(3-12).
Springer DOI 1706
BibRef

Nogueira, S.[Sarah], Sechidis, K.[Konstantinos], Brown, G.[Gavin],
On the Use of Spearman's Rho to Measure the Stability of Feature Rankings,
IbPRIA17(381-391).
Springer DOI 1706
stability to training data perturbations. BibRef

Chen, L.[Lin], Zhang, Q.A.[Qi-Ang], Li, B.X.[Bao-Xin],
Predicting Multiple Attributes via Relative Multi-task Learning,
CVPR14(1027-1034)
IEEE DOI 1409
learn ranking functions describing the relative strength of attributes. BibRef

Shankar, S.[Sukrit], Lasenby, J.[Joan], Cipolla, R.[Roberto],
Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes,
ICCV13(361-368)
IEEE DOI 1403
Ranking attributes for classification. Optimization, Ranking, Semantic Descriptions BibRef

Shi, Z.Y.[Zhi-Yuan], Siva, P.[Parthipan], Xiang, T.[Tony],
Transfer Learning by Ranking for Weakly Supervised Object Annotation,
BMVC12(78).
DOI Link 1301
BibRef

Diamantini, C.[Claudia], Gemelli, A.[Alberto], Potena, D.[Domenico],
Feature Ranking Based on Decision Border,
ICPR10(609-612).
IEEE DOI 1008
BibRef

Parakhin, M.[Mikhail], Haluptzok, P.[Patrick],
Finding the Most Probable Ranking of Objects with Probabilistic Pairwise Preferences,
ICDAR09(616-620).
IEEE DOI 0907
Ranking when pairwise ranking is inconsistent (not transitive). apply to handwriting. BibRef

Bucak, S.S.[Serhat S.], Mallapragada, P.K.[Pavan Kumar], Jin, R.[Rong], Jain, A.K.[Anil K.],
Efficient multi-label ranking for multi-class learning: Application to object recognition,
ICCV09(2098-2105).
IEEE DOI 0909
Not just binary classification. Order the many possible classes. BibRef

Merler, M.[Michele], Yan, R.[Rong], Smith, J.R.[John R.],
Imbalanced RankBoost for efficiently ranking large-scale image/video collections,
CVPR09(2607-2614).
IEEE DOI 0906
BibRef

Li, Y.[Yun], Lu, B.L.[Bao-Liang], Wu, Z.F.[Zhong-Fu],
A Hybrid Method of Unsupervised Feature Selection Based on Ranking,
ICPR06(II: 687-690).
IEEE DOI 0609
BibRef

Zhu, X.Q.[Xing-Quan], Wu, X.D.[Xin-Dong],
Scalable Representative Instance Selection and Ranking,
ICPR06(III: 352-355).
IEEE DOI 0609
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Probabilistic Latent Semantic Analysis, pLSA. .


Last update:Jul 18, 2024 at 20:50:34