Scalable Unpaired Multi-View Clustering via Anchor-Driven High-Throughput Encoding

摘要

Anchor-based strategies have become the dominant paradigm for large-scale multi-view clustering, where the quality and representational capacity of anchors are crucial to clustering performance. Existing methods typically learn anchors adaptively, focusing only on dynamically selecting anchors from the original data. However, these methods often lack an information-theoretic metric to evaluate how effectively the selected anchors capture the intrinsic characteristics of their respective clusters. Moreover, few approaches attempt to enhance the internal structure of anchor matrix to further improve clustering performance. To address these challenges, we propose a novel Anchor-Driven High-Throughput Encoding (ADHTE) framework that optimizes anchors by maximizing their throughput encoding capacity. In this method, the High-Throughput Encoding rate serves as a metric for anchor effectiveness, and we employ a deep neural network to optimize the anchor matrix. In addition, we predefine a clustering indicator matrix to construct a consistent anchor matrix across views, thereby ensuring anchor alignment. Furthermore, we propose an edge-alignment learning scheme to produce a bipartite graph with consistent edges across views. Extensive experiments on eight benchmark datasets demonstrate that the proposed ADHTE framework exhibits superior effectiveness and robustness compared to other state-of-the-art methods. The code of this paper is released on https://github.com/enjoypiker/ADHTE.