Anchor-Guided Discrete Multi-View Clustering

Multi-view clustering based on anchor graphs has attracted significant attention due to its ability to substantially reduce computational complexity, enabling the efficient processing of large-scale multimedia data. However, most existing anchor graph-based clustering methods fail to fully exploit the intrinsic properties of anchor graphs when applying regression techniques. Moreover, some approaches focus solely on sample labels while overlooking the crucial role of anchor labels in clustering. To address these limitations, we leverage the probabilistic information of the anchor graph by employing probabilistic projection to map the anchor graph into the label space, thereby obtaining anchor labels. By clustering both anchors and samples simultaneously, the anchor graph serves as a guide to induce anchor labels, which are then used to generate sample labels, facilitating anchor-guided sample clustering. Furthermore, we propose a novel regularization paradigm based on the matrix nuclear norm, ensuring that the obtained results remain discrete and that the sample distribution across clusters is balanced. Additionally, we introduce a new matrix nuclear norm optimization method based on the first-order Taylor expansion. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of our proposed method, achieving superior performance compared to state-of-the-art approaches. Our code is available at:https://github.com/harunakai/ADMC