Adaptive semi-supervised dimensionality reduction with sparse representation using pairwise constraints

Abstract

With the rapid accumulation of high dimensional data, dimensionality reduction plays a more and more important role in practical data processing and learning tasks. This paper studies semi-supervised dimensionality reduction using pairwise constraints. In this setting, domain knowledge is given in the form of pairwise 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 with Sparse Representation (ASSDR-SR) is proposed, which can get the optimized low dimensional representation of the original data by adaptively adjusting the weights of the pairwise constraints and simultaneously optimizing the graph construction using the ℓ1 graph of sparse representation. Experiments on clustering and classification tasks show that ASSDR-SR is superior to some existing dimensionality reduction methods.