Multilayer Subspace Learning With Self-Sparse Robustness for Two-Dimensional Feature Extraction

Two-dimensional (2D) feature extraction techniques are specifically designed for reducing the dimension of data in matrix representation. Existing methods mostly rely on bilateral projections of matrices. This rasterized manner critically limits the freedom of feature combinations, and thus degrades the fitting ability of models. The robustness of previous techniques is also unsatisfactory due to the insufficiency of discerning outliers with severe damages. In this work, we propose a novel bilinear subspace learning model to achieve flexible and robust two-dimensional feature extraction. The features are exploited in a multilayer bilinear low-rank space under a collaborative orthogonality constraint, and a self-sparse robust strategy is imposed. The model greatly extends the projection space and relaxes the restriction in conventional orthogonal space. We accordingly design a tactful and efficient optimization method based on the coordinate descent method, optimally addressing the proposed model. Experimental results demonstrate the excited improvements of our extracted features in image classification task.