Sparse 2-D Canonical Correlation Analysis via Low Rank Matrix Approximation for Feature Extraction

Although 2-D canonical correlation analysis (2DCCA) has been proposed to reduce the computational complexity while reserving local data structure of image, the learned canonical variables of 2DCCA are the linear combination of all the original variables, which makes it hard to interpret the solutions and might have less generality. In this paper, we propose a sparse 2-D canonical correlation analysis (S2DCCA) to solve the drawbacks of the 2DCCA method and apply it to image feature extraction. The basic idea of S2DCCA is to impose two lasso penalties on the objective function of 2DCCA to obtain two sets of sparse projection directions via low rank matrix approximation. We conduct extensive experiments on both FERET and AR databases to evaluate the performance of the proposed method.