Feature Extraction Using Class-Augmented Principal Component Analysis (CA-PCA)

  • Myoung Soo Park
  • Jin Hee Na
  • Jin Young Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Myoung Soo Park
    • 1
  • Jin Hee Na
    • 1
  • Jin Young Choi
    • 1
  1. 1.School of Electrical Engineering and Computer Science, ASRISeoul National UniversitySeoulKorea

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