A weak component approach of subspace analysis

Lijuan Pu; Weixin Xie; Jihong Pei

doi:10.1117/12.902803

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2 December 2011 A weak component approach of subspace analysis

Lijuan Pu, Weixin Xie, Jihong Pei

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Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 800404 (2011) https://doi.org/10.1117/12.902803
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

In the linear discriminative analysis, especially in the high dimension case, it is insufficient to project the data onto a one-dimensional subspace for the two-category classification problem. Therefor a weak component approach (WCA) was proposed to project patterns to a low dimensional subspace with rich number of classification features. The role of the weak component in pattern classification was discussed. And the abundance of discriminative information contained in weak components was explored. Firstly, a definition of the weak component was given. Secondly, an improved regularization method was proposed. The regularization is a biased estimate of the variance in the corresponding dimension of the training data and the population data. Then a construction method of the feature subspace based on weak component was given, which extracts the eigenvector of the scatter matrixes according to their discriminative information. Finally, the proposed approach was validated in the experiments by comparing it with LDA. A better classification accuracy of the presented method was achieved. As WCA extracts the dims on which the data distributes closer, it is applicable to the high-dimensional data which distributes elliptically.

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Lijuan Pu, Weixin Xie, and Jihong Pei "A weak component approach of subspace analysis", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 800404 (2 December 2011); https://doi.org/10.1117/12.902803

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