Robust doubly stochastic graph clustering

Abstract

Graph clustering has achieved promising performance in various real-world applications, and attracted sufficient attention in machine learning. Generally, it encodes the samples’ relationship with an affinity graph, and then conducts graph-theoretic optimization to partition the samples into clusters. The performance of the graph clustering methods may be affected by many factors, i.e., the graph quality, the loss measurement and the ad hoc post-processing. In this paper, a new Robust Doubly Stochastic graph clustering method (RDS) is presented, which has the following advantages: (1) it learns a doubly stochastic graph with the self-expression strategy automatically, and does not need the graph normalization step to improve the graph quality; (2) it utilizes a new loss function to calculate the approximation error, which is robust to the outliers that far from the normal samples; (3) it generates the cluster indicator according to the learned graph directly, such that the uncertainty caused by the post-processing procedure can be avoided. Extensive experiments on eight benchmarks demonstrate the effectiveness of RDS on data clustering, and show its advantages over the previous graph clustering methods are also verified.