Abstract
Abstract Almost all of the existing methods assume that the samples between different views have a strict one-to-one relationship whether it is for complete multi-view data or for partial multi-view data. In this paper, we refer to the neglected many-to-many relationship between cross-view samples as the complex mapping relationship between views. To address this issue, we propose a resultful Complex Mapping Multi-View Clustering (CMMVC) method by exploring the complex mapping relationship between views. We firstly construct a complex mapping relationship matrix for each pair of views by using the nearest neighbor relationship between cross-view samples. Then the complex mapping relationship matrix is introduced into the framework of multi-view clustering based on non-negative matrix factorization to guide multi-view information fusion in order to obtain more compact representation of multi-view data space. Finally, we give the objective function of CMMVC and an effective optimization scheme. The experimental results demonstrate the advantages of the proposed CMMVC method on multi-view clustering tasks by mining the complex mapping relationship between different views.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
