Fast multi-view discrete clustering with two solvers

Abstract

Multi-view graph clustering follows a three-phase process: constructing view-specific similarity graphs, fusing information from different views, and conducting eigenvalue decomposition followed by post-processing to obtain the clustering indicators. However, it encounters two key challenges: the high computational cost of graph construction and eigenvalue decomposition, and the inevitable information deviation introduced by the last process. To tackle these obstacles, we propose Fast Multi-view Discrete Clustering with two solvers (FMDC), to directly and efficiently solve the multi-view graph clustering problem. FMDC involves: (1) generating a compact set of representative anchors to construct anchor graphs, (2) automatically weighting them into a symmetric and doubly stochastic aggregated similarity matrix, (3) executing clustering on the aggregated form with the discrete indicator matrix directly computed through two efficient solvers that we devised. The linear computational complexity of FMDC w.r.t. data size is a notable improvement over traditional quadratic or cubic complexity. Extensive experiments confirm the superior performance of FMDC both in efficiency and in effectiveness.