Incomplete Multiview Clustering Using Discriminative Feature Recovery and Tensorized Matrix Factorization

Multiview clustering task groups objects using multiple properties, such as RGB images, infrared images, and texture information. However, incomplete multi-view clustering faces significant challenges due to missing views that hinder clustering performance. This paper proposes a Discriminative Feature Recovery and Tensorized Matrix Factorization method (DFRTMF) that effectively recovers missing views, learns low-dimensional discriminative embeddings, and enables direct clustering. DFRTMF addresses high dimensionality through projection learning and enables the output of soft indicators. To improve projection and facilitate the recovery of missing views, we propose an uncorrelated constraint based on the scatter matrix of the recovered complete data, exploring the correlations between observed and missing views. To capture high-order correlations among views, a low-rank tensor constraint based on tensor Schatten p-norm regularization is applied to a third-order tensor composed of soft indicator matrices. DFRTMF adaptively controls the inter-coordination between these factorizations using view weights to optimally explore complementary information. Furthermore, we propose an alternating optimization algorithm based on the Alternating Direction Method of Multipliers to effectively solve the proposed objective function. Extensive experiments across diverse datasets demonstrate the effectiveness of DFRTMF compared to the state-of-the-art methods.