Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering

Le Shu, Longin Jan Latecki
Asian Conference on Machine Learning, PMLR 45:362-377, 2016.

Abstract

Multi-view clustering takes diversity of multiple views (representations) into consideration. Multiple views may be obtained from various sources or different feature subsets and often provide complementary information to each other. In this paper, we propose a novel graph-based approach to integrate multiple representations to improve clustering performance. While original graphs have been widely used in many existing multi-view clustering approaches, the key idea of our approach is to integrate multiple views by exploring higher order information. In particular, given graphs constructed separately from single view data, we build cross-view tensor product graphs (TPGs), each of which is a Kronecker product of a pair of single-view graphs. Since each cross-view TPG captures higher order relationships of data under two different views, it is no surprise that we obtain more reliable similarities. We linearly combine multiple cross-view TPGs to integrate higher order information. Efficient graph diffusion process on the fusion TPG helps to reveal the underlying cluster structure and boosts the clustering performance. Empirical study shows that the proposed approach outperforms state-of-the-art methods on benchmark datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v45-Shu15, title = {Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering}, author = {Shu, Le and Latecki, Longin Jan}, booktitle = {Asian Conference on Machine Learning}, pages = {362--377}, year = {2016}, editor = {Holmes, Geoffrey and Liu, Tie-Yan}, volume = {45}, series = {Proceedings of Machine Learning Research}, address = {Hong Kong}, month = {20--22 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v45/Shu15.pdf}, url = {https://proceedings.mlr.press/v45/Shu15.html}, abstract = {Multi-view clustering takes diversity of multiple views (representations) into consideration. Multiple views may be obtained from various sources or different feature subsets and often provide complementary information to each other. In this paper, we propose a novel graph-based approach to integrate multiple representations to improve clustering performance. While original graphs have been widely used in many existing multi-view clustering approaches, the key idea of our approach is to integrate multiple views by exploring higher order information. In particular, given graphs constructed separately from single view data, we build cross-view tensor product graphs (TPGs), each of which is a Kronecker product of a pair of single-view graphs. Since each cross-view TPG captures higher order relationships of data under two different views, it is no surprise that we obtain more reliable similarities. We linearly combine multiple cross-view TPGs to integrate higher order information. Efficient graph diffusion process on the fusion TPG helps to reveal the underlying cluster structure and boosts the clustering performance. Empirical study shows that the proposed approach outperforms state-of-the-art methods on benchmark datasets.} }
Endnote
%0 Conference Paper %T Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering %A Le Shu %A Longin Jan Latecki %B Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Geoffrey Holmes %E Tie-Yan Liu %F pmlr-v45-Shu15 %I PMLR %P 362--377 %U https://proceedings.mlr.press/v45/Shu15.html %V 45 %X Multi-view clustering takes diversity of multiple views (representations) into consideration. Multiple views may be obtained from various sources or different feature subsets and often provide complementary information to each other. In this paper, we propose a novel graph-based approach to integrate multiple representations to improve clustering performance. While original graphs have been widely used in many existing multi-view clustering approaches, the key idea of our approach is to integrate multiple views by exploring higher order information. In particular, given graphs constructed separately from single view data, we build cross-view tensor product graphs (TPGs), each of which is a Kronecker product of a pair of single-view graphs. Since each cross-view TPG captures higher order relationships of data under two different views, it is no surprise that we obtain more reliable similarities. We linearly combine multiple cross-view TPGs to integrate higher order information. Efficient graph diffusion process on the fusion TPG helps to reveal the underlying cluster structure and boosts the clustering performance. Empirical study shows that the proposed approach outperforms state-of-the-art methods on benchmark datasets.
RIS
TY - CPAPER TI - Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering AU - Le Shu AU - Longin Jan Latecki BT - Asian Conference on Machine Learning DA - 2016/02/25 ED - Geoffrey Holmes ED - Tie-Yan Liu ID - pmlr-v45-Shu15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 45 SP - 362 EP - 377 L1 - http://proceedings.mlr.press/v45/Shu15.pdf UR - https://proceedings.mlr.press/v45/Shu15.html AB - Multi-view clustering takes diversity of multiple views (representations) into consideration. Multiple views may be obtained from various sources or different feature subsets and often provide complementary information to each other. In this paper, we propose a novel graph-based approach to integrate multiple representations to improve clustering performance. While original graphs have been widely used in many existing multi-view clustering approaches, the key idea of our approach is to integrate multiple views by exploring higher order information. In particular, given graphs constructed separately from single view data, we build cross-view tensor product graphs (TPGs), each of which is a Kronecker product of a pair of single-view graphs. Since each cross-view TPG captures higher order relationships of data under two different views, it is no surprise that we obtain more reliable similarities. We linearly combine multiple cross-view TPGs to integrate higher order information. Efficient graph diffusion process on the fusion TPG helps to reveal the underlying cluster structure and boosts the clustering performance. Empirical study shows that the proposed approach outperforms state-of-the-art methods on benchmark datasets. ER -
APA
Shu, L. & Latecki, L.J.. (2016). Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering. Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 45:362-377 Available from https://proceedings.mlr.press/v45/Shu15.html.

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