Intrinsic and Complete Structure Learning Based Incomplete Multiview Clustering

Abstract

In the real-world, some views of samples are often missing for the collected multiview data. Faced with the incomplete multiview data, most of the existing clustering methods tended to learn a common graph from the available views, where the hidden information of the absent views was ignored. Furthermore, some methods filled the absent instances with the average vector of the available samples for each view, which could not reflect a real distribution of the data. To solve these problems, in this paper an intrinsic and complete structure learning based incomplete multiview clustering method (ICSL_IMC) is proposed. Firstly, we calculate the initial complete graphs for all views by exploring the available incomplete graphs, which are further taken as the constraints for the reconstruction of the absent data integrating the self-representation method. Afterwards, encouraged by the complete multiview data, a complete structure inferring strategy is proposed to learn the intrinsic and complete structures for all views, such that the real distribution of the absent instances can be reflected in the completed structure of each view. We integrate these three learning phases into a joint optimization model, which can promote each other in the iterative learning procedure, simultaneously. Comparing with the other state-of-the-art methods, the proposed ICSL_IMC can achieve the best performances on different databases.