Improved Robust Discriminant Analysis for Feature Extraction

摘要

Dimensionality reduction (DR) has emerged as a crucial issue in developing effective pattern recognition approaches. However, the performance of many DR algorithms degrades due to the impact of noisy environment. To address this problem, we propose in this paper a novel algorithm termed as robust large margin discriminant analysis (RLMDA) in order to enhance the robustness of features. In the spirit of large margin principle as applied in support vector machine, RLMDA maximizes the minimum between-class dispersion and simultaneously minimizes the within-class dispersion in the reduced subspace. Moreover, the l₁-norm rather than traditional squared l₂-norm is exploited to describe such dispersions, making the resultant algorithm robust to noisy features. The solution of RLMDA boils down to a nonconvex and nonsmooth optimization problem. Therefore, we take advantages of both constrained concave-convex procedure (CCCP) and Lagrangian dual method, and develop an efficient iterative algorithm. Experimental results show that RLMDA achieves better performance compared with other related methods.