Discriminative Anchor Learning for Efficient Multi-view Clustering

TL;DR

This paper introduces DALMC, a novel multi-view clustering method that learns discriminative view-specific anchors and a consensus graph, improving clustering quality and computational efficiency by integrating feature learning and anchor construction.

Contribution

The paper proposes a unified framework for discriminative anchor learning in multi-view clustering, enhancing anchor quality and view-specific information utilization.

Findings

Outperforms existing methods in clustering accuracy.

Reduces computational cost compared to traditional approaches.

Demonstrates robustness across various datasets.

Abstract

Multi-view clustering aims to study the complementary information across views and discover the underlying structure. For solving the relatively high computational cost for the existing approaches, works based on anchor have been presented recently. Even with acceptable clustering performance, these methods tend to map the original representation from multiple views into a fixed shared graph based on the original dataset. However, most studies ignore the discriminative property of the learned anchors, which ruin the representation capability of the built model. Moreover, the complementary information among anchors across views is neglected to be ensured by simply learning the shared anchor graph without considering the quality of view-specific anchors. In this paper, we propose discriminative anchor learning for multi-view clustering (DALMC) for handling the above issues. We learn discriminative view-specific feature representations according to the original dataset and build anchors from different views based on these representations, which increase the quality of the shared anchor graph. The discriminative feature learning and consensus anchor graph construction are integrated into a unified framework to improve each other for realizing the refinement. The optimal anchors from multiple views and the consensus anchor graph are learned with the orthogonal constraints. We give an iterative algorithm to deal with the formulated problem. Extensive experiments on different datasets show the effectiveness and efficiency of our method compared with other methods.