Block principal component analysis with L1-norm for image analysis

Authors:
Haixian Wang
Affiliations:
Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu 210096, PR China
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

Block principal component analysis (BPCA) is an important subspace learning method in modern image analysis. The utilization of the L2-norm, however, makes it sensitive to outliers. In this paper, we propose an L1-norm-based BPCA (BPCA-L1) as a robust alternative to BPCA. We show the equivalence between the L1-norm-based two-dimensional principal component analysis (2DPCA-L1) and the L1-norm-based principal component analysis (PCA-L1), both of which can be formulated as special cases of BPCA-L1. Experiments of image reconstruction and classification on benchmark image sets show the effectiveness of the proposed method.