Principal component analysis based on l1-norm maximization.

Full 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.

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Author information

Author/s: Kwak, Nojun (N);

Affiliation: Division of Electrical and Computer Engineering, Ajou University, Suwon, Korea. nojunk@ieee.org

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2008-Sep; vol 30 (issue 9) : pp 1672-80

Dates: Created 2008/07/11; Completed 2008/09/23;

PMID: 18617723, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods

Pattern Recognition, Automated - methods
Principal Component Analysis
Reproducibility of Results
Sensitivity and Specificity

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