Multi-view Clustering with Adaptively Learned Graph

Hong Tao, Chenping Hou, Jubo Zhu, Dongyun Yi
Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:113-128, 2017.

Abstract

Multi-view clustering, which aims to improve the clustering performance by exploring the data’s multiple representations, has become an important research direction. Graph based methods have been widely studied and achieve promising performance for multi-view clustering. However, most existing multi-view graph based methods perform clustering on the fixed input graphs, and the results are dependent on the quality of input graphs. In this paper, instead of fixing the input graphs, we propose Multi-view clustering with Adaptively Learned Graph (MALG), learning a new common similarity matrix. In our model, we not only consider the importance of multiple graphs from view level, but also focus on the performance of similarities within a view from sample-pair level. Sample-pair-specific weights are introduced to exploit the connection across views in more depth. In addition, the obtained optimal graph can be partitioned into specific clusters directly, according to its connected components. Experimental results on toy and real-world datasets demonstrate the efficacy of the proposed algorithm.

Cite this Paper

BibTeX
@InProceedings{pmlr-v77-tao17a, title = {Multi-view Clustering with Adaptively Learned Graph}, author = {Tao, Hong and Hou, Chenping and Zhu, Jubo and Yi, Dongyun}, booktitle = {Proceedings of the Ninth Asian Conference on Machine Learning}, pages = {113--128}, year = {2017}, editor = {Zhang, Min-Ling and Noh, Yung-Kyun}, volume = {77}, series = {Proceedings of Machine Learning Research}, address = {Yonsei University, Seoul, Republic of Korea}, month = {15--17 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v77/tao17a/tao17a.pdf}, url = {https://proceedings.mlr.press/v77/tao17a.html}, abstract = {Multi-view clustering, which aims to improve the clustering performance by exploring the data’s multiple representations, has become an important research direction. Graph based methods have been widely studied and achieve promising performance for multi-view clustering. However, most existing multi-view graph based methods perform clustering on the fixed input graphs, and the results are dependent on the quality of input graphs. In this paper, instead of fixing the input graphs, we propose Multi-view clustering with Adaptively Learned Graph (MALG), learning a new common similarity matrix. In our model, we not only consider the importance of multiple graphs from view level, but also focus on the performance of similarities within a view from sample-pair level. Sample-pair-specific weights are introduced to exploit the connection across views in more depth. In addition, the obtained optimal graph can be partitioned into specific clusters directly, according to its connected components. Experimental results on toy and real-world datasets demonstrate the efficacy of the proposed algorithm.} }
Endnote
%0 Conference Paper %T Multi-view Clustering with Adaptively Learned Graph %A Hong Tao %A Chenping Hou %A Jubo Zhu %A Dongyun Yi %B Proceedings of the Ninth Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Min-Ling Zhang %E Yung-Kyun Noh %F pmlr-v77-tao17a %I PMLR %P 113--128 %U https://proceedings.mlr.press/v77/tao17a.html %V 77 %X Multi-view clustering, which aims to improve the clustering performance by exploring the data’s multiple representations, has become an important research direction. Graph based methods have been widely studied and achieve promising performance for multi-view clustering. However, most existing multi-view graph based methods perform clustering on the fixed input graphs, and the results are dependent on the quality of input graphs. In this paper, instead of fixing the input graphs, we propose Multi-view clustering with Adaptively Learned Graph (MALG), learning a new common similarity matrix. In our model, we not only consider the importance of multiple graphs from view level, but also focus on the performance of similarities within a view from sample-pair level. Sample-pair-specific weights are introduced to exploit the connection across views in more depth. In addition, the obtained optimal graph can be partitioned into specific clusters directly, according to its connected components. Experimental results on toy and real-world datasets demonstrate the efficacy of the proposed algorithm.
APA
Tao, H., Hou, C., Zhu, J. & Yi, D.. (2017). Multi-view Clustering with Adaptively Learned Graph. Proceedings of the Ninth Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 77:113-128 Available from https://proceedings.mlr.press/v77/tao17a.html.

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