Principal Component Analysis Based on L1-Norm Maximization

作者：Nojun Kwak · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2008 · DOI：10.1109/tpami.2008.114 · 被引用次数：795 · 研究领域：Spectroscopy and Chemometric Analyses、Advanced Algorithms and Applications、Advanced Measurement and Detection Methods

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.