Abstract
In many clustering problems, there are dozens of data which are represented by multiple views. Different views describe different aspects of the same set of instances and provide complementary information. Considering blindly combining the information from different views will degrade the multi-view clustering result, this paper proposes a novel view-weighted multi-view k-means method. Meanwhile, to reduce the adverse effect of outliers, \(l_{2,1}\) norm is employed to calculate the distance between data points and cluster centroids. An alternative iterative update schema is developed to find the optimal value. Comparative experiments on real world datasets reveal that the proposed method has better performance.
Sign-in/Register to access full text options 
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.
