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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient discriminant-based solution for small sample size problem
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
Face Recognition Using an Enhanced Independent Component Analysis Approach
IEEE Transactions on Neural Networks
A novel forward gene selection algorithm for microarray data
Neurocomputing
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On the small sample size problems such as appearance-based recognition, empirical studies have shown that ICA projections have trivial effect on improving the recognition performance over whitened PCA. However, what causes the ineffectiveness of ICA is still an open question. In this study, we find out that this small sample size problem of ICA is caused by a special distributional phenomenon of the high-dimensional whitened data: all data points are similarly distant, and nearly perpendicular to each other. In this situation, ICA algorithms tend to extract the independent features simply by the projections that isolate single or very few samples apart and congregate all other samples around the origin, without any concern on the clustering structure. Our comparative study further shows that the ICA projections usually produce misleading features, whose generalization ability is generally worse than those derived by random projections. Thus, further selection of the ICA features is possibly meaningless. To address the difficulty in pursuing low-dimensional features, we introduce a locality pursuit approach which applies the locality preserving projections in the high-dimensional whitened space. Experimental results show that the locality pursuit performs better than ICA and other conventional approaches, such as Eigenfaces, Laplacianfaces, and Fisherfaces.