An End-to-End Approach for Graph-Based Multi-View Data Clustering

Abstract

Clustering data from different sources or views is a key challenge in real-world applications. While traditional graph-based methods are effective at capturing data structures, they often require separate steps to estimate graphs of views or a consensus graph from the raw data. This reliance on intermediate steps can make these clustering methods susceptible to noisy graphs, which affects the overall performance of clustering. In response to this limitation, and with an emphasis on advocating end-to-end solutions for multi-view clustering, two comprehensive approaches are presented in this paper. Each approach starts from either the raw data or its kernelized features. The first proposal introduces a unified objective function that enables the simultaneous recovery of the graph for each view, the unified graph, the spectral projection matrices for all views, the soft cluster assignments, and the scores assigned to each view. The second proposal uses a global criterion that integrates regularization and constraints for the soft cluster assignment matrix based on the consensus graph matrix and the consensus data representation. Both proposed methods enable direct and straightforward clustering of the data without the need for additional steps. Extensive tests with various real-world image and text datasets confirm the superior performance of the two proposed methods.