Sparsity preserving projection (SPP) is a recently proposed unsupervised linear dimensionality reduction method for
face recognition, which is based on the recently-emerged sparse representation theory. It aims to find a low-dimensional
subspace to best preserve the global sparse reconstructive relationship of the original data. In this paper, we propose a
supervised variation on SPP called supervised sparsity preserving projection (SSPP). The SSPP method explicitly takes
into account the within-class weight as well as between-class weight and assigns different weights to them, which
attempts to strengthen the discriminating power and generalization ability of embedded data representation. The
effectiveness of the proposed SSPP method is verified on two standard face databases (Yale, AR).
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