-
1
-
-
44649105615
-
Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm
-
2-s2.0-44649105615 10.1016/j.patcog.2008.03.007
-
Hong Y., Kwong S., Chang Y., Ren Q., Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm. Pattern Recognition 2008 41 9 2742 2756 2-s2.0-44649105615 10.1016/j.patcog. 2008.03.007
-
(2008)
Pattern Recognition
, vol.41
, Issue.9
, pp. 2742-2756
-
-
Hong, Y.1
Kwong, S.2
Chang, Y.3
Ren, Q.4
-
2
-
-
84952503562
-
Thirteen ways to look at the correlation coefficient
-
Rodgers J. L., Nicewander W. A., Thirteen ways to look at the correlation coefficient. The American Statistician 1988 42 1 59 66
-
(1988)
The American Statistician
, vol.42
, Issue.1
, pp. 59-66
-
-
Rodgers, J.L.1
Nicewander, W.A.2
-
3
-
-
78650901005
-
Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selection
-
10.1049/iet-cvi.2009.0121 MR2789903
-
Lin J., Ming J., Crookes D., Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selection. IET Computer Vision 2011 5 1 23 32 10.1049/iet-cvi.2009.0121 MR2789903
-
(2011)
IET Computer Vision
, vol.5
, Issue.1
, pp. 23-32
-
-
Lin, J.1
Ming, J.2
Crookes, D.3
-
5
-
-
84883699144
-
Dependency based feature selection for clustering symbolic data
-
Talavera L., Nord C., Girona J., Dependency-Based Feature Selection for Clustering Symbolic Data. Intelligent Data Analysis 2000 4 1 19 28
-
(2000)
Intelligent Data Analysis
, vol.4
, Issue.1
, pp. 19-28
-
-
Talavera, L.1
Nord, C.2
Girona, J.3
-
6
-
-
79951729272
-
-
Proceedings of the 10th IEEE International Conference on Data Mining (ICDM '10) December 2010 Sydney, Australia 2-s2.0-79951729272 10.1109/ICDM.2010.137
-
Elghazel H., Aussem A., Feature selection for unsupervised learning using random cluster ensembles. Proceedings of the 10th IEEE International Conference on Data Mining (ICDM '10) December 2010 Sydney, Australia 168 175 2-s2.0-79951729272 10.1109/ICDM.2010.137
-
Feature Selection for Unsupervised Learning Using Random Cluster Ensembles
, pp. 168-175
-
-
Elghazel, H.1
Aussem, A.2
-
7
-
-
33749252873
-
-
Cambridge, Mass, USA MIT Press
-
Chapelle O., Scholkopf B., Zien A., Semi-Supervised Learning 2006 Cambridge, Mass, USA MIT Press
-
(2006)
Semi-Supervised Learning
-
-
Chapelle, O.1
Scholkopf, B.2
Zien, A.3
-
8
-
-
77952423823
-
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
-
10.1007/s10994-009-5125-7 MR3108132
-
Sugiyama M., Idé T., Nakajima S., Sese J., Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Machine Learning 2010 78 1-2 35 61 10.1007/s10994-009-5125-7 MR3108132
-
(2010)
Machine Learning
, vol.78
, Issue.1-2
, pp. 35-61
-
-
Sugiyama, M.1
Idé, T.2
Nakajima, S.3
Sese, J.4
-
9
-
-
84860387759
-
A semi-supervised feature ranking method with ensemble learning
-
2-s2.0-84860387759 10.1016/j.patrec.2012.03.001
-
Bellal F., Elghazel H., Aussem A., A semi-supervised feature ranking method with ensemble learning. Pattern Recognition Letters 2012 33 10 1426 1432 2-s2.0-84860387759 10.1016/j.patrec.2012.03.001
-
(2012)
Pattern Recognition Letters
, vol.33
, Issue.10
, pp. 1426-1432
-
-
Bellal, F.1
Elghazel, H.2
Aussem, A.3
-
10
-
-
70449102559
-
-
Proceedings of the 7th SIAM International Conference on Data Mining April 2007 2-s2.0-70449102559
-
Zhao Z., Lu H., Semi-supervised feature selection via spectral analysis. Proceedings of the 7th SIAM International Conference on Data Mining April 2007 641 646 2-s2.0-70449102559
-
Semi-supervised Feature Selection Via Spectral Analysis
, pp. 641-646
-
-
Zhao, Z.1
Lu, H.2
-
11
-
-
33746154240
-
The doubly regularized support vector machine
-
Wang L., Zhu J., Zou H., The doubly regularized support vector machine. Statistica Sinica 2006 16 2 589 615 MR2267251 ZBL1126.68070 (Pubitemid 44085519)
-
(2006)
Statistica Sinica
, vol.16
, Issue.2
, pp. 589-615
-
-
Wang, L.1
Zhu, J.2
Zou, H.3
-
13
-
-
84862300384
-
Efficient variable selection in support vector machines via the alternating direction method of multipliers
-
Guibo Y., Yifei C., Xiaohui X., Efficient variable selection in support vector machines via the alternating direction method of multipliers. Journal of Machine Learning Research 2011 15 832 840
-
(2011)
Journal of Machine Learning Research
, vol.15
, pp. 832-840
-
-
Guibo, Y.1
Yifei, C.2
Xiaohui, X.3
-
15
-
-
41549144249
-
Optimization techniques for semi-supervised support vector machines
-
Chapelle O., Sindhwani V., Keerthi S. S., Optimization techniques for semi-supervised support vector machines. Journal of Machine Learning Research 2008 9 203 233 2-s2.0-41549144249 (Pubitemid 351469022)
-
(2008)
Journal of Machine Learning Research
, vol.9
, pp. 203-233
-
-
Chapelle, O.1
Sindhwani, V.2
Keerthi, S.S.3
-
17
-
-
33747128180
-
Large scale transductive SVMs
-
Collobert R., Sinz F., Weston J., Bottou L., Large scale transductive SVMs. Journal of Machine Learning Research (JMLR) 2006 7 1687 1712 MR2274421 ZBL1222.68173 (Pubitemid 44223088)
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1687-1712
-
-
Collobert, R.1
Sinz, F.2
Weston, J.3
Bottou, L.4
-
18
-
-
0015727623
-
A dual approach to solving nonlinear programming problems by unconstrained optimization
-
MR0371416 10.1007/BF01580138 ZBL0279.90035
-
Rockafellar R. T., A dual approach to solving nonlinear programming problems by unconstrained optimization. Mathematical Programming 1973 5 354 373 MR0371416 10.1007/BF01580138 ZBL0279.90035
-
(1973)
Mathematical Programming
, vol.5
, pp. 354-373
-
-
Rockafellar, R.T.1
-
19
-
-
78650834156
-
Augmented lagrangian method, dual methods, and split bregman iteration for ROF, Vectorial TV, and high order models
-
2-s2.0-78650834156 10.1137/090767558
-
Wu C., Tai X.-C., Augmented lagrangian method, dual methods, and split bregman iteration for ROF, Vectorial TV, and high order models. SIAM Journal on Imaging Sciences 2010 3 3 300 339 2-s2.0-78650834156 10.1137/090767558
-
(2010)
SIAM Journal on Imaging Sciences
, vol.3
, Issue.3
, pp. 300-339
-
-
Wu, C.1
Tai, X.-C.2
-
20
-
-
84969334819
-
The split Bregman method for L1-regularized problems
-
Goldstein T., Osher S., The split Bregman method for L1-regularized problems. SIAM Journal on Imaging Sciences 2009 2 2 323 343
-
(2009)
SIAM Journal on Imaging Sciences
, vol.2
, Issue.2
, pp. 323-343
-
-
Goldstein, T.1
Osher, S.2
-
22
-
-
78650851477
-
Split Bregman method for large scale fused Lasso
-
2-s2.0-78650851477 10.1016/j.csda.2010.10.021
-
Ye G.-B., Xie X., Split Bregman method for large scale fused Lasso. Computational Statistics and Data Analysis 2011 55 4 1552 1569 2-s2.0-78650851477 10.1016/j.csda.2010.10.021
-
(2011)
Computational Statistics and Data Analysis
, vol.55
, Issue.4
, pp. 1552-1569
-
-
Ye, G.-B.1
Xie, X.2
-
24
-
-
80051762104
-
Distributed optimization and statistical learning via the alternating direction method of multipliers
-
2-s2.0-80051762104 10.1561/2200000016
-
Boyd S., Parikh N., Chu E., Peleato B., Eckstein J., Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 2010 3 1 1 122 2-s2.0-80051762104 10.1561/2200000016
-
(2010)
Foundations and Trends in Machine Learning
, vol.3
, Issue.1
, pp. 1-122
-
-
Boyd, S.1
Parikh, N.2
Chu, E.3
Peleato, B.4
Eckstein, J.5
|