Feature selection for identifying critical variables of principal components based on k-nearest neighbor rule

Authors:
Yun Li;Bao-Liang Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Year:
2007

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Abstract

Principal components analysis (PCA) is a popular linear feature extractor to unsupervised dimensionality reduction, and found in many branches of science including-examples in computer vision, text processing and bioinformatics, etc. However, axes of the lower-dimensional space, i.e., principal components, are a set of new variables carrying no clear physical meanings. Thus, interpretation of results obtained in the lower-dimensional PCA space and data acquisition for test samples still involve all of the original measurements. To select original features for identifying critical variables of principle components, we develop a new method with k-nearest neighbor clustering procedure and three new similarity measures to link the physically meaningless principal components back to a subset of original measurements. Experiments are conducted on benchmark data sets and face data sets with different poses, expressions, backgrounds and occlusions for gender classification to show their superiorities.