Abstract
Traditional spectral clustering methods cluster data samples with pairwise relationships usually illustrated as graphs. However, the relationships among the data in real life are much more complex than pairwise. Merely representing the complex relationships into pairwise will result in loss of information which is helpful for improving clustering results. Moreover, the data in real life are often with noise and outliers. Therefore, to solve the problems mentioned above, we introduce hypergraph to fully consider the complex relationships of the data and use the self-representation based row sparse l2,1-norm to weaken the effect of the noise. The main contribution of this work is to integrate self-representation and hypergraph together and extend graph based spectral clustering to hypergraph. After that, we propose the spectral hypergraph clustering method named Spectral Clustering based on Hypergraph and Self-representation (HGSR). Finally, we put forward an efficient optimal method to solve the proposed problem. Experiment results showed that our method prominently outperforms the graph based methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.