Sparsity Preserving Projection Based Constrained Graph Embedding and Its Application to Face Recognition

In this chapter, a novel semi-supervised dimensionality reduction algorithm is proposed, namely Sparsity Preserving Projection based Constrained Graph Embedding (SPP-CGE). Sparsity Preserving Projection (SPP) is an unsupervised dimensionality reduction method. It aims to preserve the sparse reconstructive relationship of the data obtained by solving a L1 objective function. Label information is used as additional constraints for graph embedding in the SPP-CGE algorithm. In SPP-CGE, both the intrinsic structure and the label information of the data are used. In addition, to deal with new incoming samples, out-of-sample extension of SPP-CGE is also proposed. Promising experimental results on several popular face databases illustrate the effectiveness of the proposed method.

Keywords: Affinity matrix, Constrained graph embedding, Dimensionality reduction, Eigenvalue problem, Face recognition, Graph embedding, ISOMAP, Laplacian eigenmaps, Laplacian matrix, Linear discriminant analysis, Locality preserving projection, Locally linear embedding, Multidimensional scaling, Neighborhood preserving embedding, Principal component analysis, Projection matrix, Recognition rate, Semi-supervised learning, Sparse representation, Sparsity preserving projection.