Robust Tensor Analysis with Non-Greedy L1-Norm Maximization
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Date
2016-04
Authors
ORCID
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Referee
Mark
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Volume Title
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Společnost pro radioelektronické inženýrství
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Abstract
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.
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Citation
Radioengineering. 2016 vol. 25, č. 1, s. 200-207. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2016/16_01_0012_0017.pdf
http://www.radioeng.cz/fulltexts/2016/16_01_0012_0017.pdf
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Peer-reviewed
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en