CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2008 vol.30 Issue No.09 - September
Issue No.09 - September (2008 vol.30)
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.
L1 norm optimization, principal component analysis
Nojun Kwak, "Principal Component Analysis Based on L1-Norm Maximization", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 9, pp. 1672-1680, September 2008, doi:10.1109/TPAMI.2008.114