Robust 2DPCA With Non-greedy $\ell _{1}$ -Norm Maximization for Image Analysis

2-D principal component analysis based on ℓ1-norm (2DPCA-L1) is a recently developed approach for robust dimensionality reduction and feature extraction in image domain. Normally, a greedy strategy is applied due to the difficulty of directly solving the ℓ1-norm maximization problem, which is, however, easy to get stuck in local solution. In this paper, we propose a robust 2DPCA with non-greedy ℓ1-norm maximization in which all projection directions are optimized simultaneously. Experimental results on face and other datasets confirm the effectiveness of the proposed approach.

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