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CLC number: TP391.4
On-line Access: 2012-12-09
Received: 2012-06-11
Revision Accepted: 2012-11-12
Crosschecked: 2012-11-12
Cited: 2
Clicked: 6602
Learning robust principal components from L1-norm maximization
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Ding-cheng Feng, Feng Chen, Wen-li Xu. Learning robust principal components from L1-norm maximization[J]. Journal of Zhejiang University Science C, 2012, 13(12): 901-908.
@article{title="Learning robust principal components from L1-norm maximization",
author="Ding-cheng Feng, Feng Chen, Wen-li Xu",
journal="Journal of Zhejiang University Science C",
volume="13",
number="12",
pages="901-908",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200180"
}
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T1 - Learning robust principal components from L1-norm maximization
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J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200180
Abstract: principal component analysis (PCA) is fundamental in many pattern recognition applications. Much research has been performed to minimize the reconstruction error in l1-norm based reconstruction error minimization (L1-PCA-REM) since conventional L2-norm based PCA (L2-PCA) is sensitive to outliers. Recently, the variance maximization formulation of PCA with l1-norm (L1-PCA-VM) has been proposed, where new greedy and non-greedy solutions are developed. Armed with the gradient ascent perspective for optimization, we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation, which are verified by experiments on synthetic and real-world datasets.
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