LSPC-LA: Local Structure Preserving Clustering With Learnable Anchors

K-means algorithm divides samples into c classes based on their structural characteristics. However, due to the non convex nature of the clustering problem, algorithms are prone to converge to poor local minima. To address the aforementioned issues, we propose the Local Structure Preserving Clustering with Learnable Anchors (LSPC-LA) method. We assume that with a well-designed anchor selection strategy, samples near the same anchor tend to belong to the same cluster, which reveal high confidence Must-Link local structural information for clustering. Based on this observation, we first construct an anchor-based bipartite graph, transforming the sample clustering problem into anchor clustering problem by local structural information, thus reducing the solution space and minimizing the risk of poor local minima. Then we create an anchor guiding matrix to allow anchors to learn the sample structure, improving clustering performance. Subsequently, an alternating iterative algorithm is proposed to optimize the LSPC-LA model. Finally, extensive experiments demonstrate the accuracy of the local structural information and the effectiveness of LSPC-LA.