Large-Scale Multiview Clustering via Joint Learning of Anchor Representation and Multigraph Alignment

The anchor-based clustering method is currently a predominant technique for handling large-scale data. However, in multiview data, existing anchor-based methods face a key challenge: balancing individual anchor graph distinctiveness with final consistency. To address this challenge, we propose a large-scale multiview clustering (MVC) method via joint learning of anchor representation and multigraph alignment (ARMGA). Specifically, ARMGA introduces a unified framework that facilitates the concurrent learning of single-view anchor representations and virtual graph-based multigraph alignment. The approach aims to preserve the adaptability of anchor learning across different views, while ensuring the ultimate consistency of the merged anchor graph. Furthermore, ARMGA employs Schatten- p norm on the tensor formed by the adaptive anchor representation, originating from multigraph alignment, to reinforce cross-view consistency. This technique effectively leverages complementary information preserved across views to bolster the overall structure and consensus information. Ultimately, to attenuate the noise impact on the anchor representation matrix, ARMGA capitalizes on the cosine angle information from the low-rank representation as coefficients within the relationship matrix and efficiently reduces computational complexity through deductions. On nine datasets, ARMGA has exhibited a notable improvement in clustering performance indicators by 2%-10% over other algorithms, while also maintaining lower time complexity.