Discriminative nonnegative spectral clustering with out-of-sample extension

Yang, Yang, Yang, Yi, Shen, Heng Tao, Zhang, Yanchun, Du, Xiaoyong and Zhou, Xiaofang (2013) Discriminative nonnegative spectral clustering with out-of-sample extension. IEEE Transactions on Knowledge and Data Engineering, 25 8: 1760-1771. doi:10.1109/TKDE.2012.118

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Author Yang, Yang
Yang, Yi
Shen, Heng Tao
Zhang, Yanchun
Du, Xiaoyong
Zhou, Xiaofang
Title Discriminative nonnegative spectral clustering with out-of-sample extension
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2013-08
Sub-type Article (original research)
DOI 10.1109/TKDE.2012.118
Open Access Status
Volume 25
Issue 8
Start page 1760
End page 1771
Total pages 12
Place of publication United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2014
Language eng
Formatted abstract
Data clustering is one of the fundamental research problems in data mining and machine learning. Most of the existing clustering methods, for example, normalized cut and 𝑘-means, have been suffering from the fact that their optimization processes normally lead to an NP-hard problem due to the discretization of the elements in the cluster indicator matrix. A practical way to cope with this problem is to relax this constraint to allow the elements to be continuous values. The eigenvalue decomposition can be applied to generate a continuous solution, which has to be further discretized. However, the continuous solution is probably mixing-signed. This result may cause it deviate severely from the true solution, which should be naturally nonnegative. In this paper, we propose a novel clustering algorithm, i.e., discriminative nonnegative spectral clustering, to explicitly impose an additional nonnegative constraint on the cluster indicator matrix to seek for a more interpretable solution. Moreover, we show an effective regularization term which is able to not only provide more useful discriminative information but also learn a mapping function to predict cluster labels for the out-of-sample test data. Extensive experiments on various data sets illustrate the superiority of our proposal compared to the state-of-the-art clustering algorithms.
Keyword Nonnegative spectral clustering
Discriminative regularization
Matrix factorization
Image segmentation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 27 times in Scopus Article | Citations
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