Data clustering by laplacian regularized l1 -graph

Yingzhen Yang, Zhangyang Wang, Jianchao Yang, Jiangping Wang, Shiyu Chang, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

£l -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 separately without taking into account the geometric structure of the data. Motivated by il-Graph and manifold leaning, we propose Laplacian Regularized -Graph (LRf1-Graph) for data clustering. The sparse representations of LR^1-Graph are regularized by the geometric information of the data so that they vary smoothly along the geodesies of the data manifold by the graph Laplacian according to the manifold assumption. Moreover, we propose an iterative regularization scheme, where the sparse representation obtained from the previous iteration is used to build the graph Laplacian for the current iteration of regularization. The experimental results on real data sets demonstrate the superiority of our algorithm compared to £l-Graph and other competing clustering methods.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages3148-3149
Number of pages2
ISBN (Electronic)9781577356806
StatePublished - Jan 1 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014Jul 31 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume4

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period7/27/147/31/14

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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