Abstract
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.
Highlights
Principal component analysis (PCA) is a widely used dimensionality reduction and feature extraction method due to its simplicity and effectiveness [1,2,3]
We propose a tensor principal component analysis with non-greedy 1-norm maximization termed as TPCAL1 non-greedy
It has three major advantages: 1) It is robust to outliers due to the utilization of 1-norm; 2) more spatial structure information is preserved compared with PCA-L1; 3) all projection directions can be optimized simultaneously and much better recognition accuracy can be obtained than that of TPCA-L1 without increasing the computational cost
Summary
Principal component analysis (PCA) is a widely used dimensionality reduction and feature extraction method due to its simplicity and effectiveness [1,2,3]. In 2011, Nie and Huang [13] propose a PCA with non-greedy 1-norm maximization (PCA-L1 non-greedy), in which all projection directions are optimized simultaneously without increasing computational complexity. We propose a tensor principal component analysis with non-greedy 1-norm maximization termed as TPCAL1 non-greedy. It has three major advantages: 1) It is robust to outliers due to the utilization of 1-norm; 2) more spatial structure information is preserved compared with PCA-L1; 3) all projection directions can be optimized simultaneously and much better recognition accuracy can be obtained than that of TPCA-L1 without increasing the computational cost.
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