Robust Dual Clustering with Adaptive Manifold Regularization

Zhao, N; Zhang, L; Du, B; Zhang, Q; You, J; Tao, D

Robust Dual Clustering with Adaptive Manifold Regularization

Zhao, N Zhang, L Du, B Zhang, Q You, J Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2017, 29 (11), pp. 2498 - 2509
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Zhao, N	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Du, B	en_US
dc.contributor.author	Zhang, Q	en_US
dc.contributor.author	You, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2017-11-01	en_US
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2017, 29 (11), pp. 2498 - 2509	en_US
dc.identifier.issn	1041-4347	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123858
dc.description.abstract	© 2017 IEEE. In recent years, various data clustering algorithms have been proposed in the data mining and engineering communities. However, there are still drawbacks in traditional clustering methods which are worth to be further investigated, such as clustering for the high dimensional data, learning an ideal affinity matrix which optimally reveals the global data structure, discovering the intrinsic geometrical and discriminative properties of the data space, and reducing the noises influence brings by the complex data input. In this paper, we propose a novel clustering algorithm called robust dual clustering with adaptive manifold regularization (RDC), which simultaneously performs dual matrix factorization tasks with the target of an identical cluster indicator in both of the original and projected feature spaces, respectively. Among which, the l2,1 -norm is used instead of the conventional l2-norm to measure the loss, which helps to improve the model robustness by relieving the influences by the noises and outliers. In order to better consider the intrinsic geometrical and discriminative data structure, we incorporate the manifold regularization term on the cluster indicator by using a particularly learned affinity matrix which is more suitable for the clustering task. Moreover, a novel augmented lagrangian method (ALM) based procedure is designed to effectively and efficiently seek the optimal solution of the proposed RDC optimization. Numerous experiments on the representative data sets demonstrate the superior performance of the proposed method compares to the existing clustering algorithms.	en_US
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering	en_US
dc.relation.isbasedon	10.1109/TKDE.2017.2732986	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Robust Dual Clustering with Adaptive Manifold Regularization	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	29	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	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
utslib.copyright.status	closed_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 2017 IEEE. In recent years, various data clustering algorithms have been proposed in the data mining and engineering communities. However, there are still drawbacks in traditional clustering methods which are worth to be further investigated, such as clustering for the high dimensional data, learning an ideal affinity matrix which optimally reveals the global data structure, discovering the intrinsic geometrical and discriminative properties of the data space, and reducing the noises influence brings by the complex data input. In this paper, we propose a novel clustering algorithm called robust dual clustering with adaptive manifold regularization (RDC), which simultaneously performs dual matrix factorization tasks with the target of an identical cluster indicator in both of the original and projected feature spaces, respectively. Among which, the l2,1 -norm is used instead of the conventional l2-norm to measure the loss, which helps to improve the model robustness by relieving the influences by the noises and outliers. In order to better consider the intrinsic geometrical and discriminative data structure, we incorporate the manifold regularization term on the cluster indicator by using a particularly learned affinity matrix which is more suitable for the clustering task. Moreover, a novel augmented lagrangian method (ALM) based procedure is designed to effectively and efficiently seek the optimal solution of the proposed RDC optimization. Numerous experiments on the representative data sets demonstrate the superior performance of the proposed method compares to the existing clustering algorithms.

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http://hdl.handle.net/10453/123858