Discriminative Sparsity Preserving Projections for Semi-Supervised Dimensionality Reduction

In this letter, we propose a semi-supervised dimensionality reduction method named Discriminative Sparsity Preserving Projection (DSPP). In order to get the feature mapping which projects the high-dimensional data into a low-dimensional intrinsic space, DSPP attempts to maintain the prior low-dimensional representation constructed by the data points and the known class labels and, meanwhile, considers the complexity of in the ambient space and the smoothness of in preserving the sparse representation of data. On one hand, the DSPP method obtains an explicit nonlinear feature mapping for the out-of-sample extrapolation. On the other hand, the DSPP method has a high discriminative ability which is inherited from the sparse representation of data. Experiment results show the effectiveness of the proposed method.