Regularized l1-Graph for Data Clustering

Yingzhen Yang, Zhangyang Wang, Jianchao Yang, Jiawei Han and Thomas Huang

In Proceedings British Machine Vision Conference 2014


l1-Graph has been proven to be effective in data clustering, which partitions the data space by using the sparse representation of the data as the similarity measure. However, the sparse representation is performed for each datum independently without taking into account the geometric structure of the data. Motivated by l1-Graph and manifold leaning, we propose Regularized l1-Graph (Rl1-Graph) for data clustering. Compared to l1-Graph, the sparse representations of Rl1-Graph are regularized by the geometric information of the data. In accordance with the manifold assumption, the sparse representations vary smoothly along the geodesics of the data manifold through the similarity matrix constructed by the sparse codes. Experimental results on various data sets demonstrate the superiority of our algorithm compared to l1-Graph and other competing clustering methods.


Poster Session


Extended Abstract (PDF, 1 page, 44K)
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Bibtex File


Yingzhen Yang, Zhangyang Wang, Jianchao Yang, Jiawei Han, and Thomas Huang. Regularized l1-Graph for Data Clustering. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.


	title = {Regularized l1-Graph for Data Clustering},
	author = {Yang, Yingzhen and Wang, Zhangyang and Yang, Jianchao and Han, Jiawei and Huang, Thomas},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
	doi = { }