Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization.

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Multi-view clustering has garnered significant attention due to its capacity to utilize information from multiple perspectives. The concept of anchor graph-based techniques was introduced to manage large-scale data better. However, current methods rely on K-means or uniform sampling to select anchors in the original space. This results in a disjointed approach separating anchor selection and subsequent graph construction. Moreover, these methods typically require additional K-means or spectral clustering to derive labels, often leading to suboptimal outcomes. To address these challenges, we present a novel approach called Multi-view Clustering based on Feature Selection and Semi-Non-Negative Anchor Graph Factorization (MCFSAF). This method unifies feature selection, anchor and anchor graph learning, and semi-non-negative factorization of the anchor graph into a cohesive framework. Within this framework, the anchors and anchor graph are learned in the embedding space following feature selection, and the clustering indicator matrix is obtained via semi-non-negative factorization of the anchor graph in each view. By applying the minimization of the tensor Schatten p-norm, we can uncover complementary information across multiple views efficiently. This synergetic process of anchor selection, anchor graph learning, and indicator matrix updating can effectively enhance the clustering quality. Critically, the fused indicator matrix enables us to directly acquire clustering labels without requiring additional K-means, thereby significantly improving the stability of the clustering process. Our method is optimized via an alternating iterations algorithm. Comprehensive experimental evaluations underscore the superior performance of our approach.