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Discriminative nonnegative spectral clustering with out-of-sample extension

Yang, Yang, Yang, Yi, Shen, HT, Zhang, Yanchun ORCID: 0000-0002-5094-5980, Du, X and Zhou, X (2013) Discriminative nonnegative spectral clustering with out-of-sample extension. IEEE Transactions on Knowledge and Data Engineering, 25 (8). 1760 - 1771. ISSN 1041-4347

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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 mixingsigned. 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-ofthe- art clustering algorithms

Item Type: Article
Uncontrolled Keywords: Discriminative regularization; Nonnegative spectral clustering; Out-of-sample
Subjects: FOR Classification > 0804 Data Format
FOR Classification > 0806 Information Systems
Faculty/School/Research Centre/Department > College of Science and Engineering
Depositing User: Symplectic Elements
Date Deposited: 16 Apr 2015 01:06
Last Modified: 11 Jan 2017 12:50
URI: http://vuir.vu.edu.au/id/eprint/26054
DOI: https://doi.org/10.1109/TKDE.2012.118
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Citations in Scopus: 48 - View on Scopus

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