NDLI: Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR)

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Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR)

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Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR)

Content Provider	ACM Digital Library
Author	Cook, Diane J. Feuz, Kyle D.
Copyright Year	2015
Abstract	Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces.
Starting Page	1
Ending Page	27
Page Count	27
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2629528
Volume Number	6
Issue Number	1
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Heterogeneous transfer learning Activity recognition Domain adaption Text classification
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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