Sparse L1-norm-based linear discriminant analysis

Gui-Fu Lu

doi:10.1007/s11042-017-5193-9

Sparse L1-norm-based linear discriminant analysis

2017-09-13

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DOI：10.1007/s11042-017-5193-9

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Linear discriminant analysis (LDA) is a well-known feature extraction method, which has been widely used for many pattern recognition problems. However, the objective function of conventional LDA is based on L2-norm, which makes LDA sensitive to outliers. Besides, the basis vectors learned by conventional LDA are dense and it is often hard to explain the extracted features. In this paper, we propose a novel sparse L1-norm-based linear discriminant analysis (SLDA-L1) which not only replaces L2-norm in conventional LDA with L1-norm, but also use the elastic net to regularize the basis vectors. Then L1-norm used in SLDA-L1 is for both robust and sparse modelling simultaneously. We also propose an efficient iterative algorithm to solve SLDA-L1 which is theoretically shown to arrive at a locally maximal point. Experiment results on some image databases demonstrate the effectiveness of the proposed method.

Keywords:

Feature extraction

Dimensionality reduction

L1-norm

L2-norm

elastic net regularization

Sparse L1-norm-based linear discriminant analysis

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