Robust Principal Component Analysis with Non-Greedy l1-Norm Maximization
Feiping Nie, Heng Huang, Chris Ding, Dijun Luo, Hua Wang
IJCAI - 2011
Principal Component Analysis (PCA) is one of the most important methods to handle high-dimensional data. However, the high computational complexity makes it hard to apply to the large scale data with high dimensionality, and the used l2-norm makes it sensitive to outliers. A recent work proposed principal component analysis based on l1-norm maximization, which is efficient and robust to outliers. In that work, a greedy strategy was applied due to the difficulty of directly solving the l1-norm maximization problem, which is easy to get stuck in local solution. In this paper, we first propose an efficient optimization algorithm to solve a general l1-norm maximization problem, and then propose a robust principal component analysis with non-greedy l1-norm maximization. Experimental results on real world datasets show that the nongreedy method always obtains much better solution than that of the greedy method.
Links
- View publications from Hua Wang
- View publications presented in IJCAI
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Cite this paper
MLA
Nie, Feiping, et al. "Robust principal component analysis with non-greedy l1-norm maximization." IJCAI Proceedings-International Joint Conference on Artificial Intelligence. Vol. 22. No. 1. 2011.
BibTeX
@inproceedings{nie2011robust,
title={Robust principal component analysis with non-greedy $\ell_1$-norm maximization},
author={Nie, Feiping and Huang, Heng and Ding, Chris and Luo, Dijun and Wang, Hua},
booktitle={IJCAI Proceedings-International Joint Conference on Artificial Intelligence},
volume={22},
number={1},
pages={1433},
year={2011}
}