Robust tensor clustering with non-greedy maximization

Cao, X; Wei, X; Han, Y; Yang, Y; Lin, D

Robust tensor clustering with non-greedy maximization

Cao, X Wei, X Han, Y Yang, Y

Lin, D

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2013, pp. 1254 - 1259
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Cao, X	en_US
dc.contributor.author	Wei, X	en_US
dc.contributor.author	Han, Y	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Lin, D	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2013, pp. 1254 - 1259	en_US
dc.identifier.isbn	9781577356332	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120783
dc.description.abstract	Tensors are increasingly common in several areas such as data mining, computer graphics, and computer vision. Tensor clustering is a fundamental tool for data analysis and pattern discovery. However, there usually exist outlying data points in realworld datasets, which will reduce the performance of clustering. This motivates us to develop a tensor clustering algorithm that is robust to the outliers. In this paper, we propose an algorithm of Robust Tensor Clustering (RTC). The RTC firstly finds a lower rank approximation of the original tensor data using a L1 norm optimization function. Because the L1 norm doesn't exaggerate the effect of outliers compared with L2 norm, the minimization of the L1 norm approximation function makes RTC robust to outliers. Then we compute the HOSVD decomposition of this approximate tensor to obtain the final clustering results. Different from the traditional algorithm solving the approximation function with a greedy strategy, we utilize a non-greedy strategy to obtain a better solution. Experiments demonstrate that RTC has better performance than the state-ofthe- art algorithms and is more robust to outliers.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Robust tensor clustering with non-greedy maximization	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Tensors are increasingly common in several areas such as data mining, computer graphics, and computer vision. Tensor clustering is a fundamental tool for data analysis and pattern discovery. However, there usually exist outlying data points in realworld datasets, which will reduce the performance of clustering. This motivates us to develop a tensor clustering algorithm that is robust to the outliers. In this paper, we propose an algorithm of Robust Tensor Clustering (RTC). The RTC firstly finds a lower rank approximation of the original tensor data using a L1 norm optimization function. Because the L1 norm doesn't exaggerate the effect of outliers compared with L2 norm, the minimization of the L1 norm approximation function makes RTC robust to outliers. Then we compute the HOSVD decomposition of this approximate tensor to obtain the final clustering results. Different from the traditional algorithm solving the approximation function with a greedy strategy, we utilize a non-greedy strategy to obtain a better solution. Experiments demonstrate that RTC has better performance than the state-ofthe- art algorithms and is more robust to outliers.

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http://hdl.handle.net/10453/120783