One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning
This work addresses large-scale multi-view clustering problems, offering incremental improvements in efficiency and effectiveness for data analysis applications.
The paper tackles the issues of redundant information and noise in existing anchor graph-based multi-view clustering methods, as well as inefficiencies from separate post-processing, by proposing a one-step method that integrates adaptive low-rank anchor-graph learning and category indicator acquisition into a unified framework, resulting in improved clustering effectiveness and efficiency on both ordinary and large-scale datasets.
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. To overcome the aforementioned issues, we deliver a novel one-step multi-view clustering method with adaptive low-rank anchor-graph learning (OMCAL). To 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.