Adaptive manifold regularized matrix factorization for data clustering

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

Adaptive manifold regularized matrix factorization for data clustering

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

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2017, pp. 3399 - 3405
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Zhang, Q	en_US
dc.contributor.author	Du, B	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	2017-01-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2017, pp. 3399 - 3405	en_US
dc.identifier.isbn	9780999241103	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126333
dc.description.abstract	Data clustering is the task to group the data samples into certain clusters based on the relationships of samples and structures hidden in data, and it is a fundamental and important topic in data mining and machine learning areas. In the literature, the spectral clustering is one of the most popular approaches and has many variants in recent years. However, the performance of spectral clustering is determined by the affinity matrix, which is usually computed by a predefined model (e.g., Gaussian kernel function) with carefully tuned parameters combination, and may not optimal in practice. In this paper, we propose to consider the observed data clustering as a robust matrix factorization point of view, and learn an affinity matrix simultaneously to regularize the proposed matrix factorization. The solution of the proposed adaptive manifold regularized matrix factorization (AMRMF) is reached by a novel Augmented Lagrangian Multiplier (ALM) based algorithm. The experimental results on standard clustering datasets demonstrate the superior performance over the exist alternatives.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Adaptive manifold regularized matrix factorization for data clustering	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US

Abstract:

Data clustering is the task to group the data samples into certain clusters based on the relationships of samples and structures hidden in data, and it is a fundamental and important topic in data mining and machine learning areas. In the literature, the spectral clustering is one of the most popular approaches and has many variants in recent years. However, the performance of spectral clustering is determined by the affinity matrix, which is usually computed by a predefined model (e.g., Gaussian kernel function) with carefully tuned parameters combination, and may not optimal in practice. In this paper, we propose to consider the observed data clustering as a robust matrix factorization point of view, and learn an affinity matrix simultaneously to regularize the proposed matrix factorization. The solution of the proposed adaptive manifold regularized matrix factorization (AMRMF) is reached by a novel Augmented Lagrangian Multiplier (ALM) based algorithm. The experimental results on standard clustering datasets demonstrate the superior performance over the exist alternatives.

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http://hdl.handle.net/10453/126333