Regularized direct linear graph embedding

Abstract

Linear Graph Embedding (LGE) is the linearization of graph embedding, which could explain many of the popular dimensionality reduction algorithms such as LDA, LLE and LPP. LGE algorithms have been applied in many domains successfully; however, those algorithms need a PCA transform in advance to avoid a possible singular problem. In this paper, a regularized direct linear graph embedding algorithm is proposed by imposing Tikhonov regularizer on the objective function of LGE. Further, we extract features from the original data set directly by solving common Eigen value problem of symmetric positive semi definite matrix. Experimental results demonstrate the effectiveness and robustness of our proposed algorithm.