Discrete nonnegative spectral clustering

Yang, Yang, Shen, Fumin, Huang, Zi, Shen, Heng Tao and Li, Xuelong (2017) Discrete nonnegative spectral clustering. IEEE Transactions On Knowledge and Data Engineering, 29 9: 1834-1845. doi:10.1109/TKDE.2017.2701825

Author Yang, Yang
Shen, Fumin
Huang, Zi
Shen, Heng Tao
Li, Xuelong
Title Discrete nonnegative spectral clustering
Journal name IEEE Transactions On Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2017-09-01
Sub-type Article (original research)
DOI 10.1109/TKDE.2017.2701825
Open Access Status Not yet assessed
Volume 29
Issue 9
Start page 1834
End page 1845
Total pages 12
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1710 Information Systems
1706 Computer Science Applications
1703 Computational Theory and Mathematics
Abstract Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with ℓ 2,p loss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches.
Keyword Discrete optimization
Spectral clustering
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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