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Principal Component Analysis Based on L1-Norm Maximization
September 2008 (vol. 30 no. 9)
pp. 1672-1680
Nojun Kwak, Ajou University, Suwon
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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Index Terms:
L1 norm optimization, principal component analysis
Citation:
Nojun Kwak, "Principal Component Analysis Based on L1-Norm Maximization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1672-1680, Sept. 2008, doi:10.1109/TPAMI.2008.114
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