Random mixed field model for mixed-attribute data restoration

Li, Q; Bian, W; Xu, RYD; You, J; Tao, D

Random mixed field model for mixed-attribute data restoration

Li, Q

Bian, W Xu, RYD

You, J Tao, D

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Publication Type:: Conference Proceeding
Citation:: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 1244 - 1250
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Li, Q https://orcid.org/0000-0002-4384-8226	en_US
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Xu, RYD https://orcid.org/0000-0003-2080-4762	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	2016-01-01	en_US
dc.identifier.citation	30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 1244 - 1250	en_US
dc.identifier.isbn	9781577357605	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122959
dc.description.abstract	© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Noisy and incomplete data restoration is a critical preprocessing step in developing effective learning algorithms, which targets to reduce the effect of noise and missing values in data. By utilizing attribute correlations and/or instance similarities, various techniques have been developed for data denoising and imputation tasks. However, current existing data restoration methods are either specifically designed for a particular task, or incapable of dealing with mixed-attribute data. In this paper, we develop a new probabilistic model to provide a general and principled method for restoring mixed-attribute data. The main contributions of this study are twofold: A) a unified generative model, utilizing a generic random mixed field (RMF) prior, is designed to exploit mixedattribute correlations; and b) a structured mean-field variational approach is proposed to solve the challenging inference problem of simultaneous denoising and imputation. We evaluate our method by classification experiments on both synthetic data and real benchmark datasets. Experiments demonstrate, our approach can effectively improve the classification accuracy of noisy and incomplete data by comparing with other data restoration methods.	en_US
dc.relation.ispartof	30th AAAI Conference on Artificial Intelligence, AAAI 2016	en_US
dc.title	Random mixed field model for mixed-attribute data restoration	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Noisy and incomplete data restoration is a critical preprocessing step in developing effective learning algorithms, which targets to reduce the effect of noise and missing values in data. By utilizing attribute correlations and/or instance similarities, various techniques have been developed for data denoising and imputation tasks. However, current existing data restoration methods are either specifically designed for a particular task, or incapable of dealing with mixed-attribute data. In this paper, we develop a new probabilistic model to provide a general and principled method for restoring mixed-attribute data. The main contributions of this study are twofold: A) a unified generative model, utilizing a generic random mixed field (RMF) prior, is designed to exploit mixedattribute correlations; and b) a structured mean-field variational approach is proposed to solve the challenging inference problem of simultaneous denoising and imputation. We evaluate our method by classification experiments on both synthetic data and real benchmark datasets. Experiments demonstrate, our approach can effectively improve the classification accuracy of noisy and incomplete data by comparing with other data restoration methods.

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http://hdl.handle.net/10453/122959