Robust tri-factor decomposition for multi-view clustering

Abstract

Multi-view clustering has garnered widespread attention due to its ability to effectively mine complementary and consistent information across different views. However, traditional methods often suffer from high computational complexity, making them unsuitable for large-scale data scenarios. Although matrix factorization based multi-view clustering approaches have alleviated scalability issues to some extent, they still exhibit three main limitations: 1) Most methods fail to consider pervasive noise interference in large-scale data, resulting in significantly degraded discriminative capability and robustness in complex noisy environments; 2) They typically treat views of varying qualities equally, preventing full exploitation of the rich discriminative information in high-quality views; 3) Most methods separate clustering partition from coefficient matrices learning, preventing clustering partitions from guiding discriminative coefficient learning and thereby limiting clustering performance. To address the aforementioned issues, this paper proposes a Robust Tri-Factor Decomposition for Multi-view Clustering (RTFDMC) method. RTFDMC incorporates the \( l_{2,1} \)-norm when constructing coefficient matrices, effectively suppressing noise interference and enhancing model robustness; meanwhile, it employs a self-supervised weighting mechanism that fully leverages information from high-quality views; furthermore, RTFDMC employs tri-factor decomposition to tightly couple clustering partitioning with representation learning, thereby improving the discriminative capability of the coefficient matrices and further enhancing clustering performance. Comprehensive experiments on eight datasets validate the effectiveness of the proposed method.