Robust Tensor Analysis with Non-Greedy L1-Norm Maximization
Alternativní metriky PlumXhttp://hdl.handle.net/11012/57928
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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.
Typ dokumentuRecenzovaný dokument
Zdrojový dokumentRadioengineering. 2016 vol. 25, č. 1, s. 200-207. ISSN 1210-2512
- 2016/1