Low-Rank Double Relaxed Regression for Discriminative Projection Learning

In many pattern recognition and computer vision applications, the observed high dimensional data often contains redundant information which can lead to increased computational complexity and overfitting. Most classification approaches learn discriminative projections with respect to the strict binary label matrix. This results in overfitting and loss of intrinsic structure of the observed data. To overcome these limitations, we propose low-rank double relaxed regression (LR-DRR) for image classification. LR-DRR optimizes the projections using a relaxed target matrix for regression based on the class label information. The proposed work extracts the projections while jointly considering a relaxed target matrix and a low-rank error term in the regression framework so as to allow double relaxation. Numerical experiments on several public data sets for face recognition, object classification, and scene classification applications show that the proposed approach is able to identify low-dimensional discriminative features and outperforms the state of the art classification methods.