Manifold elastic net for sparse learning

In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation. Face based gender recognition has received much attention in the psychophysical and video surveillance literatures. Most of existing works apply the appearance based information for data representation. A face image with size 40 by 40 could be seen as a point in a linear space with 1600 dimensions. For gender recognition, we have two classes (male and female) in total, so it is essential to find a small number of variables for representation to generalize duly. MEN can duly find the intrinsic structure of a dataset for separating males from the females. Sufficient experimental results on FERET and UMIST datasets suggest that MEN is more effective in selecting discriminative variables for face based gender recognition compared to principal component analysis, sparse principal component analysis, and discriminative locality alignment.