Journal
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Volume -, Issue -, Pages 1585-1590Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICPR.2014.281
Keywords
Linear discriminant analysis; Robust feature extraction; L1-norm; Projected subgradient method
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Feature extraction plays an important role in analyzing data with multivariate features. Linear discriminant analysis based on L1-norm (LDA-L1) is a recently developed technique for enhancing the robustness of the classic LDA against outliers. However, LDA-L1 employs a greedy strategy to find all the discriminant vectors, which may lead to suboptimal solution. To address this issue, we develop a novel algorithm termed as ILDA-L1 in this paper, which can optimize all the discriminant vectors simultaneously in a unified framework. Specifically, we introduce an orthonormal constraint on the discriminant vectors and convert the objective function of LDAL-1 into a difference formula. To solve the resulting nonconvex and nonsmooth problem, we first construct a successive concave approximation to the objective function at current solution and then use projected subgradient method, thus leading to a convergent iterative algorithm. The experimental results on several benchmark datasets confirm the effectiveness of ILDA-L1 in extracting robust features.
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