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- Publisher Website: 10.1109/TNNLS.2015.2472457
- Scopus: eid_2-s2.0-85027557459
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Article: Learning Transferred Weights from Co-Occurrence Data for Heterogeneous Transfer Learning
Title | Learning Transferred Weights from Co-Occurrence Data for Heterogeneous Transfer Learning |
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Authors | |
Keywords | directed cyclic network (DCN) heterogeneous transfer learning multidomain transferred weight Co-occurrence data |
Issue Date | 2016 |
Citation | IEEE Transactions on Neural Networks and Learning Systems, 2016, v. 27, n. 11, p. 2187-2200 How to Cite? |
Abstract | © 2012 IEEE. One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning. |
Persistent Identifier | http://hdl.handle.net/10722/276545 |
ISSN | 2021 Impact Factor: 14.255 2020 SCImago Journal Rankings: 2.882 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, Liu | - |
dc.contributor.author | Jing, Liping | - |
dc.contributor.author | Yu, Jian | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:33:56Z | - |
dc.date.available | 2019-09-18T08:33:56Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2016, v. 27, n. 11, p. 2187-2200 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/276545 | - |
dc.description.abstract | © 2012 IEEE. One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.subject | directed cyclic network (DCN) | - |
dc.subject | heterogeneous transfer learning | - |
dc.subject | multidomain | - |
dc.subject | transferred weight | - |
dc.subject | Co-occurrence data | - |
dc.title | Learning Transferred Weights from Co-Occurrence Data for Heterogeneous Transfer Learning | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNNLS.2015.2472457 | - |
dc.identifier.scopus | eid_2-s2.0-85027557459 | - |
dc.identifier.volume | 27 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 2187 | - |
dc.identifier.epage | 2200 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.identifier.isi | WOS:000386940300004 | - |
dc.identifier.issnl | 2162-237X | - |