Orthogonal self-guided similarity preserving projections

In this paper, we propose a novel unsupervised dimensionality reduction (DR) method called orthogonal self-guided similarity preserving projections (OSSPP), which seamlessly integrates the procedures of an adjacency graph learning and DR into a one step. Specifically, OSSPP projects the data into a low-dimensional subspace and simultaneously performs similarity preserving learning by using the similarity preserving regularization term in which the reconstruction coefficients of the projected data are used to encode the similarity structure information. An interesting finding is that the problem to determine the reconstruction coefficients can be converted into a weighted non-negative sparse coding problem without any explicit sparsity constraint. Thus the projections obtained by OSSPP contain natural discriminating information. Experimental results demonstrate that OSSPP outperforms state-of-the-art methods in DR.