让锚点均匀分布在各簇内，提升多视图聚类效果
Anchor Learning with Potential Cluster Constraints for Multi-view Clustering
- 通过潜在簇约束强制锚点从各簇内部生成
- 联合优化锚点学习与图构建，提升聚类中心代表性
- 适合需要高质量锚点的多视图聚类任务
基于锚点的多视图聚类（MVC）因其高效性能受到广泛关注。现有方法仅关注如何从原始数据中动态学习锚点并构建描述样本与锚点关系的锚图进行聚类，却忽略了锚点的真实特性：高质量锚点应均匀分布于不同数据簇内，而非散落在簇外。为此，本文提出一种新方法——带潜在簇约束的锚点学习多视图聚类（ALPC）。ALPC首先建立共享潜在语义模块，约束锚点从特定簇中生成；随后，通过自适应调整锚图，分别捕捉样本与锚点的共同聚类中心质量，提升锚点的表征力与区分度；最终，将锚点学习与图构建统一于协同优化框架中，实现互惠增强。大量实验表明，该方法优于多项先进MVC方法。代码已开源：https://github.com/whbdmu/ALPC。
原文摘要 · Abstract (English)
Anchor-based multi-view clustering (MVC) has received extensive attention due to its efficient performance. Existing methods only focus on how to dynamically learn anchors from the original data and simultaneously construct anchor graphs describing the relationships between samples and perform clustering, while ignoring the reality of anchors, i.e., high-quality anchors should be generated uniformly from different clusters of data rather than scattered outside the clusters. To deal with this problem, we propose a noval method termed Anchor Learning with Potential Cluster Constraints for Multi-view Clustering (ALPC) method. Specifically, ALPC first establishes a shared latent semantic module to constrain anchors to be generated from specific clusters, and subsequently, ALPC improves the representativeness and discriminability of anchors by adapting the anchor graph to capture the common clustering center of mass from samples and anchors, respectively. Finally, ALPC combines anchor learning and graph construction into a unified framework for collaborative learning and mutual optimization to improve the clustering performance. Extensive experiments demonstrate the effectiveness of our proposed method compared to some state-of-the-art MVC methods. Our source code is available at https://github.com/whbdmu/ALPC.
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