Graph optimization for dimensionality reduction with sparsity constraints

Graph-based dimensionality reduction (DR) methods play an increasingly important role in many machine learning and pattern recognition applications. In this paper, we propose a novel graph-based learning scheme to conduct Graph Optimization for Dimensionality Reduction with Sparsity Constraints (GODRSC). Different from most of graph-based DR methods where graphs are generally constructed in advance, GODRSC aims to simultaneously seek a graph and a projection matrix preserving such a graph in one unified framework, resulting in an automatically updated graph. Moreover, by applying an l1 regularizer, a sparse graph is achieved, which models the “locality” structure of data and contains natural discriminating information. Finally, extensive experiments on several publicly available UCI and face databases verify the feasibility and effectiveness of the proposed method.

► This paper proposes a new dimensionality reduction method with adjustable graph. ► It addresses the issues in GoLPP, such as nonsparse graph and uncertain projections. ► It bridges GoLPP and SPP, as well as gets the advantages of the both worlds.