2016-02-25-Shu15.md

title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	author	date	address	publisher	container-title	volume	genre	issued	pdf
Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering	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.	inproceedings	Proceedings of Machine Learning Research	Shu15	0	Integration of Single-view Graphs with Diffusion of Tensor Product Graphs for Multi-view Spectral Clustering	362	377	362-377	362	false	given family Le Shu given family Longin Jan Latecki	2016-02-25	Hong Kong	PMLR	Asian Conference on Machine Learning	45	inproceedings	date-parts 2016 2 25	http://proceedings.mlr.press/v45/Shu15.pdf