Principal Component Analysis Based on L1-Norm Maximization

Authors:
Nojun Kwak
Affiliations:
Ajou University, Suwon
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2008

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Abstract

A method of principal component analysis (PCA) based on a new L1-norm optimization technique is proposed. Unlike conventional PCA which is based on L2-norm, the proposed method is robust to outliers because it utilizes L1-norm which is less sensitive to outliers. It is invariant to rotations as well. The proposed L1-norm optimization technique is intuitive, simple, and easy to implement. It is also proven to find a locally maximal solution. The proposed method is applied to several datasets and the performances are compared with those of other conventional methods.