Sparse bilinear preserving projections

The techniques of linear dimensionality reduction have been attracted widely attention in the fields of computer vision and pattern recognition. In this paper, we propose a novel framework called Sparse Bilinear Preserving Projections (SBPP) for image feature extraction. We generalized the image-based bilinear preserving projections into sparse case for feature extraction. Different from the popular bilinear linear projection techniques, the projections of SBPP are sparse, i.e. most elements in the projections are zeros. In the proposed framework, we use the local neighborhood graph to model the manifold structure of the data set at first, and then spectral analysis and L₁-norm regression by using the Elastic Net are combined together to iteratively learn the sparse bilinear projections, which optimal preserve the local geometric structure of the image manifold. Experiments on some databases show that SBPP is competitive to some state-of-the-art techniques.