Robust Tensor Analysis with Non-Greedy L1-Norm Maximization

L. Zhao, W. Jia, R. Wang, Q Yu

Robust Tensor Analysis with Non-Greedy L1-Norm Maximization

Číslo: 1/2016
Periodikum: Radioengineering Journal
DOI: 10.13164/re.2016.0200

Klíčová slova: Principal component analysis (PCA), TPCA, L1-norm, outliers, non-greedy strategy, Analýza hlavních komponent (PCA), TPCA, norma L1, extrémní výsledky

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Anotace: The L1-norm based tensor analysis (TPCA-L1) is recently proposed for dimensionality reduction and feature extraction. However, a greedy strategy was utilized for solving the L1-norm maximization problem, which makes it prone to being stuck in local solutions. In this paper, we propose a robust TPCA with non-greedy L1-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.