Adaptive Semi-Supervised Dimensionality Reduction

Abstract

With the rapid accumulation of high dimensional data, dimensionality reduction plays a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pair wise constraints. In this setting, domain knowledge is given in the form of pair wise constraints, which specifies whether a pair of instances belong to the same class (must-link constraint) or different classes (cannot-link constraint). In this paper, a novel semi-supervised dimensionality reduction method called Adaptive Semi-Supervised Dimensionality Reduction (ASSDR) is proposed, which can get the optimized low dimensional representation of the original data by adaptively adjusting the weights of the pair wise constraints and simultaneously optimizing the graph construction. Experiments on UCI classification and face recognition show that ASSDR is superior to many existing dimensionality reduction methods.