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L1-norm-based principal component analysis with adaptive regularization.
- Source :
-
Pattern Recognition . Dec2016, Vol. 60, p901-907. 7p. - Publication Year :
- 2016
-
Abstract
- Recently, some L1-norm-based principal component analysis algorithms with sparsity have been proposed for robust dimensionality reduction and processing multivariate data. The L1-norm regularization used in these methods encounters stability problems when there are various correlation structures among data. In order to overcome the drawback, in this paper, we propose a novel L1-norm-based principal component analysis with adaptive regularization (PCA-L1/AR) which can consider sparsity and correlation simultaneously. PCA-L1/AR is adaptive to the correlation structure of the training samples and can benefit both from L2-norm and L1-norm. An iterative procedure for solving PCA-L1/AR is also proposed. The experiment results on some data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 60
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 117800721
- Full Text :
- https://doi.org/10.1016/j.patcog.2016.07.014