Robust Tensor Analysis with Non Greedy L1 Norm Maximization
The `1 norm based tensor analysis (TPCA L1) is recently proposed for dimensionality reduction and feature extraction. However, a greedy strategy was utilized for solving the `1 norm maximization problem, which makes it prone to being stuck in local solutions. In this paper, we propose a robust TPCA with non greedy `1 norm maximization (TPCA L1 non greedy) in which all projection directions are optimized simultaneously. Experiments on several face databases demonstrate the effectiveness of the proposed method.