Tensor-based Low-dimensional Representation Learning for Multi-view Clustering.

Abstract

With the development of data collection techniques, multi-view clustering becomes an emerging research direction to improve the clustering performance. Existing work 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 heterogenous 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, 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 selfexpressive 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.