Feature extraction using class-augmented principal component analysis (CA-PCA)

Authors:
Myoung Soo Park;Jin Hee Na;Jin Young Choi
Affiliations:
School of Electrical Engineering and Computer Science, ASRI, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Science, ASRI, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Science, ASRI, Seoul National University, Seoul, Korea
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper, we propose a novel feature extraction method called Class-Augmented PCA (CA-PCA) which uses class information. The class information is augmented to data and influences the extraction of features so that the features become more appropriate for classification than those from original PCA. Compared to other supervised feature extraction methods LDA and its variants, this scheme does not use the scatter matrix including inversion and therefore it is free from the problems of LDA originated from this matrix inversion. The performance of the proposed scheme is evaluated by experiments using two well-known face database and as a result we can show that the performance of the proposed CA-PCA is superior to those of other methods.