Tensor-Based Low-Dimensional Representation Learning for Multi-View Clustering

With the development of data collection techniques, multi-view clustering becomes an emerging research direction to improve the clustering performance. This paper has shown that leveraging multi-view information is able to provide a rich and comprehensive description. One of the core problems is how to sufficiently represent multi-view data in the analysis. In this paper, we introduce a tensor-based representation learning method for multi-view clustering (tRLMvC) that can unify heterogeneous and high-dimensional multi-view feature spaces to a low-dimensional shared latent feature space and improve multi-view clustering performance. To sufficiently capture plenty multi-view information, the tRLMvC represents multi-view data as a third-order tensor, expresses each tensorial data point as a sparse $t$ -linear combination of all data points with $t$ -product, and constructs a self-expressive tensor through reconstruction coefficients. The low-dimensional multi-view data representation in the shared latent feature space can be obtained via Tucker decomposition on the self-expressive tensor. These two parts are iteratively performed so that the interaction between self-expressive tensor learning and its factorization can be enhanced and the new representation can be effectively generated for clustering purpose. We conduct extensive experiments on eight multi-view data sets and compare the proposed model with the state-of-the-art methods. Experimental results have shown that tRLMvC outperforms the baselines in terms of various evaluation metrics.