Enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures

摘要

Multi-view clustering seeks to partition objects based on various observations by utilizing cross-views to provide a complementary description of the same objects. It remains challenging to effectively fuse the multi-view data with various dimensions as well as different structures into a new yet highly informative form, thus facilitating adequate assignment of the objects. To tackle the issue, we propose a common subspace integration (CSI) model. The CSI actively learns a common subspace by jointly preserving the local geometry of each view, while incorporating a global partition information to enhance its separability during the learning process. It can be easily generalized to its kernel version, thereby popularizing its general usages. An effective alternative optimization scheme is designed to solve the proposed model. Extensive experiments on six real-world datasets were conducted to demonstrate its superiority by comparing with the twelve state-of-art methods. (C) 2019 Elsevier B.V. All rights reserved.