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Locality-Constrained Transfer Coding for Heterogeneous Domain Adaptation

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Databases Theory and Applications (ADC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10538))

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Abstract

Currently, most of widely used databases are label-wise. In other words, people organize their data with corresponding labels, e.g., class information, keywords and description, for the convenience of indexing and retrieving. However, labels of the data from a novel application usually are not available, and labeling by hand is very expensive. To address this, we propose a novel approach based on transfer learning. Specifically, we aim at tackling heterogeneous domain adaptation (HDA). HDA is a crucial topic in transfer learning. Two inevitable issues, feature discrepancy and distribution divergence, get in the way of HDA. However, due to the significant challenges of HDA, previous work commonly focus on handling one of them and neglect the other. Here we propose to deploy locality-constrained transfer coding (LCTC) to simultaneously alleviate the feature discrepancy and mitigate the distribution divergence. Our method is powered by two tactics: feature alignment and distribution alignment. The former learns new transferable feature representations by sharing-dictionary coding and the latter aligns the distribution gaps on the new feature space. By formulating the problem into a unified objective and optimizing it via an iterative fashion, the two tactics are reinforced by each other and the two domains are drawn closer under the new representations. Extensive experiments on image classification and text categorization verify the superiority of our method against several state-of-the-art approaches.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61371183, ARC under Grant FT130101530 and DP170103954, the Applied Basic Research Program of Sichuan Province under Grant 2015JY0124, and the China Scholarship Council.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Jingjing Li & Ke Lu

  2. School of Information Technology and Electrical Engineering, University of Queensland, Queensland, QLD, 4067, Australia

    Jingjing Li & Lei Zhu

  3. Beijing Etrol Technologies Co., Ltd., Beijing, China

    Zhihui Li

Authors
  1. Jingjing Li
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  2. Ke Lu
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  3. Lei Zhu
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  4. Zhihui Li
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Corresponding author

Correspondence to Jingjing Li .

Editor information

Editors and Affiliations

  1. University of Queensland, Brisbane, Queensland, Australia

    Zi Huang

  2. Nanyang Technological University, Singapore, Singapore

    Xiaokui Xiao

  3. University of New South Wales, Sydney, New South Wales, Australia

    Xin Cao

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Li, J., Lu, K., Zhu, L., Li, Z. (2017). Locality-Constrained Transfer Coding for Heterogeneous Domain Adaptation. In: Huang, Z., Xiao, X., Cao, X. (eds) Databases Theory and Applications. ADC 2017. Lecture Notes in Computer Science(), vol 10538. Springer, Cham. https://doi.org/10.1007/978-3-319-68155-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-68155-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68154-2

  • Online ISBN: 978-3-319-68155-9

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