Chapter 2

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

Libo Weng, Zhong Jin and Fadi Dornaika


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.

Total Pages: 23-28 (6)

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