Large-scale multi-view spectral clustering based on two-stage well-distributed anchor selection

Abstract

Spectral clustering has attracted much attention because of its good clustering effect, but its high computational cost makes it difficult to apply to large-scale multi-view clustering. In response to this issue, a simple and efficient large-scale multi-view spectral clustering algorithm is proposed, which is based on a Two-stage Well-distributed Anchor Selection strategy (TWAS). Firstly, the data set is divided into several disjoint sample blocks to get the global well-distributed anchor candidate. Then, the algorithm proceeds to select anchor points within each local candidate anchor set. This two-stage anchor selection strategy facilitates the identification of anchors with significant representativeness at a reduced computational expense, thereby adeptly capturing the intrinsic data structure. Secondly, the present study devises an adaptive near-neighbor graph learning approach to construct an anchor-based intra-view similarity matrix. Finally, the multiple views are fused to obtain a consistent inter-view similarity matrix, and the clustering results are obtained. Extensive experiments demonstrate the effectiveness, efficiency, and stability of the TWAS algorithm.