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http://dx.doi.org/10.5391/JKIIS.2005.15.4.492

PCA-based Feature Extraction using Class Information  

Park, Myoung-Soo (서울대학교 전기컴퓨터 공학부, 자동화시스템 공동연구소(ASRI))
Na, Jin-Hee (서울대학교 전기컴퓨터 공학부, 자동화시스템 공동연구소(ASRI))
Choi, Jin-Young (서울대학교 전기컴퓨터 공학부, 자동화시스템 공동연구소(ASRI))
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.4, 2005 , pp. 492-497 More about this Journal
Abstract
Feature extraction is important to classify data with large dimension such as image data. The representative feature extraction methods lot feature extraction ate PCA, ICA, LDA and MLP, etc. These algorithms can be classified in two groups: unsupervised algorithms such as PCA, LDA, and supervised algorithms such as LDA, MLP. Among these two groups, supervised algorithms are more suitable to extract the features for classification because of the class information of input data. In this paper we suggest a new feature extraction algorithm PCA-FX which uses class information with PCA to extract ieatures for classification. We test our algorithm using Yale face database and compare the performance of proposed algorithm with those of other algorithms.
Keywords
PCA-FX;
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