Supervised manifold-preserving graph reduction for noisy data classification

Abstract

Data reduction has become one of essential techniques in current knowledge discovery scenarios, dominated by noisy data. The manifold-preserving graph reduction (MPGR) algorithm has been proposed, which has the advantages of eliminating the influence of outliers and noisy and simultaneously accelerating the evaluation of predictors learned from manifolds. Based on MPGR, this paper utilizes the label information to guide the construction of graph and presents a supervised MPGR (SMPGR) method for classification tasks. In addition, we construct a similarity matrix using kernel tricks and develop the kernelized version for SMPGR. Empirical experiments on several datasets show the efficiency of the proposed algorithms.