Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
This work addresses the problem of incomplete multi-view clustering for researchers and practitioners in machine learning, offering a unified framework that is incremental in nature.
The paper tackles the challenge of clustering with incomplete multi-view data by proposing a method that leverages local and complementary information to learn a common latent representation, which significantly improves clustering performance as demonstrated in experiments.
Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specifically, the proposed method simultaneously exploits the local information of each view and the complementary information among views to learn the common latent representation for all samples, which can greatly improve the compactness and discriminability of the obtained representation. Compared with the conventional graph embedding methods, the proposed method does not introduce any extra regularization term and corresponding penalty parameter to preserve the local structure of data, and thus does not increase the burden of extra parameter selection. By imposing the orthogonal constraint on the basis matrix of each view, the proposed method is able to handle the out-of-sample. Moreover, the proposed method can be viewed as a unified framework for multi-view learning since it can handle both incomplete and complete multi-view clustering and classification tasks. Extensive experiments conducted on several multi-view datasets prove that the proposed method can significantly improve the clustering performance.