Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86081
Title: Multi-label metric transfer learning jointly considering instance space and label space distribution divergence
Authors: Jiang, Siyu
Xu, Yonghui
Wang, Tengyun
Yang, Haizhi
Qiu, Shaojian
Yu, Han
Song, Hengjie
Keywords: Transfer Learning
Metric Learning
DRNTU::Engineering::Computer science and engineering
Issue Date: 2019
Source: Jiang, S., Xu, Y., Wang, T., Yang, H., Qiu, S., Yu, H., & Song, H. (2019). Multi-label metric transfer learning jointly considering instance space and label space distribution divergence. IEEE Access, 7, 10362-10373. doi:10.1109/ACCESS.2018.2889572
Series/Report no.: IEEE Access
Abstract: Multi-label learning deals with problems in which each instance is associated with a set of labels. Most multi-label learning algorithms ignore the potential distribution differences between the training domain and the test domain in the instance space and label space, as well as the intrinsic geometric information of the label space. These restrictive assumptions limit the ability of the existing multi-label learning algorithms to classify between domains. To solve this problem, in this paper, we propose a novel distribution-adaptation-based method, the multi-label metric transfer learning (MLMTL), to relax these two assumptions and handle more general multi-label learning tasks effectively. In particular, MLMTL extends the maximum mean discrepancy method into multi-label classification by learning and adjusting the weights for the multi-labeled training instances. In this way, MLMTL bridges the instance distribution and label distribution divergence between training and test datasets. In addition, based on the balanced multi-label training data, we explore the intrinsic geometric information of the label space by encoding it into a distance metric learning framework. Extensive experiments on five benchmark datasets show that the proposed approach significantly outperforms the state-of-the-art multi-label learning algorithms.
URI: https://hdl.handle.net/10356/86081
http://hdl.handle.net/10220/48323
DOI: 10.1109/ACCESS.2018.2889572
Rights: © 2019 IEEE (Open Access). Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.