Robust Discriminative Principal Component Analysis

摘要

Least square regression (LSR) and principal component analysis (PCA) are two representative dimensionality reduction algorithms in the fields of machine learning. In this paper, we propose a novel method to jointly learn projections from the subspaces derived from the modified LSR and PCA. To implement simultaneous feature learning, we design a novel joint regression learning model by imposing two orthogonal constraints. Therefore, the learned projections can preserve the minimum reconstruction error and the discriminative information in the low-dimensional subspaces. Besides, since the traditional LSR and PCA are sensitive to the outliers, we utilize the robust L_(2,1)-norm as the metric of loss function to improve the model's robustness. A simple iterative algorithm is proposed to solve the proposed framework. Experiments on face databases show the promising performance of our method.