An improved locality preserving projection with ℓ1-norm minimization for dimensionality reduction

Abstract

Abstract Locality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the l2-norm criterion for optimization, and is thus sensitive to outliers. In order to achieve robustness, LPP-L1 is proposed by employing the l1-norm as distance criterion. However, the edge weights of LPP-L1 measure only the dissimilarity of pairs of vertices and ignore the preservation of the similarity. In this paper, we develop a novel algorithm, termed as ILPP-L1, in which the l1-norm is utilized to obtain robustness and the similarities of pairs of vertices are effectively preserved, simultaneously. ILPP-L1 is robust to outliers because of the use of the l1-norm. The l1-norm minimization problem is directly solved, which ensures the preservation of the similarity of pairs of vertices. The solution is justified to converge to local minimum. In addition, ILPP-L1 avoids small sample size problem. Experiment results on benchmark databases confirm the effectiveness of the proposed method.