Presentation	2012-06-15 A fast implementation of a subspace method using PCA-L1 Mariko HIROKAWA, Yoshimitsu KUROKI,
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Abstract(in English)	The subspace method generates subspaces for each class, and assigns an input data to the subspace that expresses most aptly. To generate the subspaces, you generally use PCA (principal component analysis), but it is sensitive to outliers. Owing to relieve the influence of outliers, one uses PCA-L1 (PCA based on Li-norm maximization) in exchange for computational loads. PCA-L1 needs initial vector for each basis, and to get the initial vector takes a lot of time. This paper proposes obtaining the initial vector using Gram-Schmidt orthogonalization. The proposed method reduces the computational loads on the subspace method using PCA-L1, and outperform the subspace method using PCA.
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Keyword(in English)	subspace method / principal component analysis based on L1-norm maximization
Paper #	SIS2012-12
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Committee	SIS
Conference Date	2012/6/7(1days)
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Registration To	Smart Info-Media Systems (SIS)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	A fast implementation of a subspace method using PCA-L1
Sub Title (in English)
Keyword(1)	subspace method
Keyword(2)	principal component analysis based on L1-norm maximization
1st Author's Name	Mariko HIROKAWA
1st Author's Affiliation	Kurume National College of Technology()
2nd Author's Name	Yoshimitsu KUROKI
2nd Author's Affiliation	Kurume National College of Technology
Date	2012-06-15
Paper #	SIS2012-12
Volume (vol)	vol.112
Number (no)	78
Page	pp.pp.-
#Pages	6
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