Incomplete multi-view clustering with cosine similarity

Abstract

Incomplete multi-view clustering partitions multi-view data suffering from missing views, for which matrix factorization approaches seek the latent representation of incomplete multi-view data and constitute one effective category of methods. To exploit data properties further, manifold structure preserving is also incorporated into matrix factorization. However, previous methods optimized the data similarity matrix in the manifold structure preserving term as an unknown variable, which is not guaranteed to faithfully represent the similarities of the original multi-view data and also increases the computational difficulty. To overcome these drawbacks, in this paper, we propose Incomplete Multi-view Clustering with Cosine Similarity (IMCCS). In IMCCS, we directly calculate the cosine similarity in the original multi-view space to strengthen the ability of preserving the manifold structure of the original multi-view data. There is no need to introduce the additional variable. The manifold structure preserving term with cosine similarity and the matrix factorization term are integrated into a unified objective function. An iterative algorithm with gradient descent is designed to solve this objective. Extensive experiments on multi-view datasets show that IMCCS outperforms state-of-the-art incomplete multi-view clustering methods.