Incomplete Multi-View Clustering via Robust Representation Learning and Tensor-Based Co-Regularization

As incompleteness is common in real-world data, incomplete multi-view clustering is of great significance in the unsupervised learning field because it allows the partitioning of multi-view data with missing information into distinct groups. In this paper, we propose a novel generalized framework for incomplete multi-view clustering based on robust representation learning and tensor-based co-regularization (RRLTCR). Specifically, a robust principal component analysis is first used to learn a robust representation for each view. To explore high-order relationships among views, the view-specific spectral embeddings are stacked into a third-order tensor with a Schattenp-norm constraint. By spreading the complementary information of the high-quality available data from each view on a global scale, our model is able to alleviate the adverse effects of data noise and uncover the underlying common cluster structure. An effective iterative optimization strategy is developed to efficiently solve our model. According to the experimental results on seven datasets, our proposed framework has the potential to improve the clustering performance for a variety of incomplete multi-view clustering problems. Our research work brings a generalized framework for incomplete multi-view clustering, which can also assist in exploring the large cohort of existing incomplete multimodality datasets for other downstream tasks.