2019/08/12 by Zhaohong Deng, Chen Cui, Deng, Zhaohong +12
Computer Science · Mathematics · Physics and Astronomy · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.04771
This paper has been submitted to IEEE Transactions on Cybnetics in Apr. 8th 2019
arxiv created 2019/08/12 · openalex publication_date 2019/08/12 · arxiv updated 2019/08/14 · openalex created_date 2019/08/22 · openalex updated_date 2026/07/28
As the multi-view data grows in the real world, multi-view clus-tering has become a prominent technique in data mining, pattern recognition, and machine learning. How to exploit the relation-ship between different views effectively using the characteristic of multi-view data has become a crucial challenge. Aiming at this, a hidden space sharing multi-view fuzzy clustering (HSS-MVFC) method is proposed in the present study. This method is based on the classical fuzzy c-means clustering model, and obtains associ-ated information between different views by introducing shared hidden space. Especially, the shared hidden space and the fuzzy partition can be learned alternatively and contribute to each other. Meanwhile, the proposed method uses maximum entropy strategy to control the weights of different views while learning the shared hidden space. The experimental result shows that the proposed multi-view clustering method has better performance than many related clustering methods.