Sparsity Preserving Discriminant Projections with Applications to Face Recognition

Abstract EN

Dimensionality reduction is extremely important for understanding the intrinsic structure hidden in high-dimensional data.

In recent years, sparse representation models have been widely used in dimensionality reduction.

In this paper, a novel supervised learning method, called Sparsity Preserving Discriminant Projections (SPDP), is proposed.

SPDP, which attempts to preserve the sparse representation structure of the data and maximize the between-class separability simultaneously, can be regarded as a combiner of manifold learning and sparse representation.

Specifically, SPDP first creates a concatenated dictionary by classwise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least square method.

Secondly, a local between-class separability function is defined to characterize the scatter of the samples in the different submanifolds.

Then, SPDP integrates the learned sparse representation information with the local between-class relationship to construct a discriminant function.

Finally, the proposed method is transformed into a generalized eigenvalue problem.

Extensive experimental results on several popular face databases demonstrate the feasibility and effectiveness of the proposed approach.