Learning Robust Discriminant Subspace Based on Joint L2, p- and L2,s-Norm Distance Metrics

Recently, there are many works on discriminant analysis, which promote the robustness of models against outliers by using L-1- or L-2,L-1-norm as the distance metric. However, both of their robustness and discriminant power are limited. In this article, we present a new robust discriminant subspace (RDS) learning method for feature extraction, with an objective function formulated in a different form. To guarantee the subspace to be robust and discriminative, we measure the within-class distances based on L-2,L-s-norm and use L-2,L- p-norm to measure the between-class distances. This also makes our method include rotational invariance. Since the proposed model involves both L-2,(p)-norm maximization and L-2,L-s-norm minimization, it is very challenging to solve. To address this problem, we present an efficient nongreedy iterative algorithm. Besides, motivated by trace ratio criterion, a mechanism of automatically balancing the contributions of different terms in our objective is found. RDS is very flexible, as it can be extended to other existing feature extraction techniques. An in-depth theoretical analysis of the algorithm's convergence is presented in this article. Experiments are conducted on several typical databases for image classification, and the promising results indicate the effectiveness of RDS.

Automated summary

In this article, a new robust discriminant subspace (RDS) learning method is presented for feature extraction. The method uses a different objective function formulation to ensure the subspace is both robust and discriminative. An efficient nongreedy iterative algorithm is proposed to solve the challenging optimization problem. The experimental results on image classification databases demonstrate the effectiveness of RDS.