Enhanced supervised locality preserving projections for face recognition

Abstract

To address the problem of "curse of dimensionality", usually dimensionality reduction is used to reduce data's dimensionalities. As a graph-based method for linear dimensionality reduction, Locality Preserving Projections (LPP) searches for an embedding space in which the similarity among the local neighborhoods is preserved. However, LPP has two disadvantages: Firstly, LPP doesn't take the label information into consideration which is crucial for classification tasks; Secondly, like most graph-based methods, graph construction of LPP is sensitive to noise and outliers. To these end, we propose an Enhanced Supervised LPP(ESLPP) that allows both locality and class label information to be incorporated which improves the performance of classification. In the mean time, ESLPP uses similarity based on robust path instead of Gaussian heat kernel similarity such that it can capture the underlying geometric distribution of samples even when there are noise and outliers. Experimental results on face databases confirm its effectiveness.