One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning

Abstract--In light of their capability to capture structural information while reducing computing complexity, anchor graph-based multi-view clustering (AGMC) methods have attracted considerable attention in large-scale clustering problems. Nevertheless, existing AGMC methods still face the following two issues: 1) They directly embedded diverse anchor graphs into a consensus anchor graph (CAG), and hence ignore redundant information and numerous noises contained in these anchor graphs, leading to a decrease in clustering effectiveness; 2) They drop effectiveness and efficiency due to independent post-processing to acquire clustering indicators. T o overcome the aforementioned issues, we deliver a novel one-step multi-view clustering method with adaptive low-rank anchor-graph learning (OMCAL). T o construct a high-quality CAG, OMCAL provides a nuclear norm-based adaptive CAG learning model against information redundancy and noise interference. Then, to boost clustering effectiveness and efficiency substantially, we incorporate category indicator acquisition and CAG learning into a unified framework. Numerous studies conducted on ordinary and large-scale datasets indicate that OMCAL outperforms existing state-of-the-art methods in terms of clustering effectiveness and efficiency. Index T erms--Multi-view clustering, low-rank graph, anchor graph, matrix decomposition. HE rapid development of multimedia technology and information technology has led to the explosive growth of multi-view data. In the realm of multi-view clustering [1], [2], graph-based multi-view clustering (GMC) [3], [4], [5], [6] methods have garnered significant attention for their capacity to capture rich structural information within the given data. Zhiping Lin is with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.