NDLI: PCA-based feature extraction using class information

Content Provider	IEEE Xplore Digital Library
Author	Myoung Soo Park Jin Hee Na Jin Young Choi
Copyright Year	2005
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Seoul Nat. Univ., South Korea (Myoung Soo Park; Jin Hee Na; Jin Young Choi)
Abstract	Feature extraction is necessary to classify a data with large dimension such as image data. It is important that the obtained features include the maximum information of input data. The representative methods for feature extraction are PCA, ICA, LDA and MLP etc. PCA, LDA are unsupervised type algorithms, and LDA, MLP are supervised type algorithms. Supervised type algorithms are more suitable for feature extraction because of using input data with class information. In this paper, we suggest the feature extraction scheme which uses class information to extract features by PCA. We test our algorithm using Yale face database and analyze the performance to compare with other algorithms.
Starting Page	341
Ending Page	345
File Size	298488
Page Count	5
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Principal component analysis Linear discriminant analysis Independent component analysis Data mining Testing Spatial databases Data analysis Performance analysis Algorithm design and analysis Yale face database feature extraction PCA class information dimension reduction face recognition
Content Type	Text
Resource Type	Article

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