In recent years, multi-view clustering has become a hot research topic due to the increasing amount of multi-view data. Among existing multi-view clustering methods, proximity-based method is a typical class and achieves much success. Usually, these methods need proximity matrices as inputs, which can be constructed by some nearest-neighbors-based approaches. However, in this way, neither the intra-view cluster structure nor the inter-view correlation is considered in constructing proximity matrices. To address this issue, we propose a novel method, named multi-view proximity learning. By introducing the idea of representative, our model can consider both the relations between data objects and the cluster structure within individual views. Besides, the spectral-embedding-based scheme is adopted for modeling the correlations across different views, i.e. the view consistency and complement properties. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method.
CITATION STYLE
Lin, K. Y., Huang, L., Wang, C. D., & Chao, H. Y. (2018). Multi-view proximity learning for clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10828 LNCS, pp. 407–423). Springer Verlag. https://doi.org/10.1007/978-3-319-91458-9_25
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