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research-article

Quadruple Transfer Learning

Authors:

Lei LiAuthors Info & Claims

Knowledge-Based Systems, Volume 90, Issue C

Pages 199 - 210

https://doi.org/10.1016/j.knosys.2015.09.017

Published: 01 December 2015 Publication History

Abstract

Transfer learning focuses on leveraging the knowledge in source domains to complete the learning tasks in target domains, where the data distributions of the source and target domains are related but different in accordance with original features. To tackle the challenge of different data distributions, previous methods mine the high-level concepts (e.g., feature clusters) from original features, which shows to be suitable for the classification. The general strategies of the previous approaches are to utilize the identical concepts, the synonymous concepts or both of them as shared concepts to establish the bridge between the source and target domains. Besides the shared concepts, some methods use the different concepts for training model. Specifically, these methods assume that the identical concepts (e.g., feature clusters) in different domains can be mapped to the same example classes. However, some ambiguous concepts may exist in different domains and result in misleading classification in the target domains. Therefore, we need a general transfer learning framework, which can exploit four kinds of concepts including the identical concepts, the synonymous concepts, the different concepts and the ambiguous concepts simultaneously, for cross-domain classification.In this paper, we present a novel method, Quadruple Transfer Learning (QTL), which models these four kinds of concepts together to fit different situations on the data distributions. In addition, an iterative algorithm with convergence guarantee based on non-negative matrix tri-factorization techniques is presented to solve the optimization problem. Finally, systematic experiments demonstrate that QTL is more effective than all the compared baselines.

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Cited By

Khan FQamar UBashir S(2019)SWIMSKnowledge-Based Systems10.1016/j.knosys.2016.02.011100:C(97-111)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.knosys.2016.02.011
Khan FQamar UBashir S(2019)Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3187-923:14(5431-5442)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3187-9
Liu L(2018)An effective dimensionality reduction method for text classification based on TFP-treeJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17123834:3(1893-1905)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/JIFS-171238
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Information

Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 90, Issue C

December 2015

263 pages

ISSN:0950-7051

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Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2015

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Cited By

Khan FQamar UBashir S(2019)SWIMSKnowledge-Based Systems10.1016/j.knosys.2016.02.011100:C(97-111)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.knosys.2016.02.011
Khan FQamar UBashir S(2019)Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3187-923:14(5431-5442)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3187-9
Liu L(2018)An effective dimensionality reduction method for text classification based on TFP-treeJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17123834:3(1893-1905)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/JIFS-171238
Liu BXiao YHao Z(2018)A Selective Multiple Instance Transfer Learning Method for Text Categorization ProblemsKnowledge-Based Systems10.1016/j.knosys.2017.11.019141:C(178-187)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.knosys.2017.11.019

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