A Survey on Concept Factorization: From Shallow to Deep Representation Learning

Zhang, Z; Zhang, Y; Xu, M; Zhang, L; Yang, Y; Yan, S

A Survey on Concept Factorization: From Shallow to Deep Representation Learning

Zhang, Z Zhang, Y Xu, M Zhang, L Yang, Y

Yan, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Processing and Management, 2021, 58, (3)
Issue Date:: 2021-05-01

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Zhang, Y
dc.contributor.author	Xu, M
dc.contributor.author	Zhang, L
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.contributor.author	Yan, S
dc.date.accessioned	2022-03-07T04:08:03Z
dc.date.available	2022-03-07T04:08:03Z
dc.date.issued	2021-05-01
dc.identifier.citation	Information Processing and Management, 2021, 58, (3)
dc.identifier.issn	0306-4573
dc.identifier.issn	1873-5371
dc.identifier.uri	http://hdl.handle.net/10453/155015
dc.description.abstract	The quality of obtained features by representation learning determines the performance of a learning algorithm and subsequent application tasks (e.g., high-dimensional data clustering). As an effective paradigm for learning representations, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Moreover, lots of effective CF-based methods have been proposed based on different perspectives and properties, but it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from current studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing current methods. Specifically, we first review the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Information Processing and Management
dc.relation.isbasedon	10.1016/j.ipm.2021.102534
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0804 Data Format, 0806 Information Systems, 0807 Library and Information Studies
dc.subject.classification	Information & Library Sciences
dc.title	A Survey on Concept Factorization: From Shallow to Deep Representation Learning
dc.type	Journal Article
utslib.citation.volume	58
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-07T04:07:58Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	58
utslib.citation.issue	3

Abstract:

The quality of obtained features by representation learning determines the performance of a learning algorithm and subsequent application tasks (e.g., high-dimensional data clustering). As an effective paradigm for learning representations, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Moreover, lots of effective CF-based methods have been proposed based on different perspectives and properties, but it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from current studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing current methods. Specifically, we first review the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.

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http://hdl.handle.net/10453/155015