Deep embedding based tensor incomplete multi-view clustering

Abstract

The majority of multi-view data are extracted from real life, which often lose information in some views. To solve this problem, existing incomplete multi-view clustering algorithms explore the valuable information from incomplete data while populating the missing information. Nevertheless, they suffer from the following two limitations: 1) The non-linear relationships of high-dimensional available data are not considered. 2) Although some methods utilize information from different views to fill in missing information, they still cannot precisely explore the missing information of each view. To this end, this article proposes a novel one-step incomplete multi-view framework, referred to as deep embedding based tensor incomplete multi-view clustering (DETIMC). Concretely, in this framework, the high-dimensional available data are projected into the low-dimensional embedding space by deep non-negative matrix factorization (NMF), which can obtain a clean space while capturing the complex non-linear relationship of available data. Moreover, a novel double-enhanced missing-view inferring strategy is developed, in which the weighted higher-order information of different views and the clustering structure information are simultaneously exploited. Comprehensive experiments on several benchmark datasets exhibit the superiority of DETIMC over conventional and state-of-the-art algorithms.