Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 30, Issue 9, Pages 1672-1680Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2008.114
Keywords
PCA-L1; L1-norm; optimization; principal component analysis; robust
Funding
- National Research Foundation of Korea [2007-313-D00610] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
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 the 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 data sets and the performances are compared with those of other conventional methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available