Simple One-Step Multi-View Clustering With Fast Similarity and Cluster Structure Learning

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency.

I. Introduction

Data clustering is a fundamental technique in signal processing and Big Data analytics [1], [2], [3]. With the rapid development of information technology, massive multi-view data have emerged, which pose significant challenges to the current data clustering research [4]. To seek a consistent clustering decision by fusing the information of multiple sources (or views), many multi-view clustering (MVC) methods have been developed recently [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. Despite the considerable progress, there are still several critical limitations to these existing MVC methods.