Partition level multiview subspace clustering
| Autor: | Zenglin Xu, Joey Tianyi Zhou, Hongyuan Zhu, Xi Peng, Chong Peng, Xinjia Zhao, Zhao Kang, Wenyu Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Basic premise Computer science Cognitive Neuroscience 02 engineering and technology computer.software_genre Partition (database) Graph 020901 industrial engineering & automation Data point Subspace clustering Artificial Intelligence Consensus clustering 0202 electrical engineering electronic engineering information engineering Cluster Analysis Learning Graph (abstract data type) 020201 artificial intelligence & image processing Data mining Cluster analysis computer Algorithms |
| Zdroj: | Neural Networks. 122:279-288 |
| ISSN: | 0893-6080 |
| DOI: | 10.1016/j.neunet.2019.10.010 |
| Popis: | Multiview clustering has gained increasing attention recently due to its ability to deal with multiple sources (views) data and explore complementary information between different views. Among various methods, multiview subspace clustering methods provide encouraging performance. They mainly integrate the multiview information in the space where the data points lie. Hence, their performance may be deteriorated because of noises existing in each individual view or inconsistent between heterogeneous features. For multiview clustering, the basic premise is that there exists a shared partition among all views. Therefore, the natural space for multiview clustering should be all partitions. Orthogonal to existing methods, we propose to fuse multiview information in partition level following two intuitive assumptions: (i) each partition is a perturbation of the consensus clustering; (ii) the partition that is close to the consensus clustering should be assigned a large weight. Finally, we propose a unified multiview subspace clustering model which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition. These three components are seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution. Experiments on four benchmark datasets demonstrate the efficacy of our approach against the state-of-the-art techniques. |
| Databáze: | OpenAIRE |
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