Robust tensor clustering with non-greedy maximization

Cao, Xiaochun, Wei, Xingxing, Han, Yahong, Yang, Yi and Lin, Dongdai (2013). Robust tensor clustering with non-greedy maximization. In: IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, (1254-1259). 3-9 August 2013.

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Name Description MIMEType Size Downloads
Author Cao, Xiaochun
Wei, Xingxing
Han, Yahong
Yang, Yi
Lin, Dongdai
Title of paper Robust tensor clustering with non-greedy maximization
Conference name 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
Conference location Beijing, China
Conference dates 3-9 August 2013
Proceedings title IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence   Check publisher's open access policy
Place of Publication Menlo Park, CA, United States
Publisher AAAI Press / International Joint Conferences on Artificial Intelligence
Publication Year 2013
Sub-type Fully published paper
Open Access Status
ISBN 9781577356332
ISSN 1045-0823
Start page 1254
End page 1259
Total pages 6
Collection year 2014
Abstract/Summary 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
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Author post-print permissible

 
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