research-article

Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation

Authors:

Michael K. NgAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 1

Article No.: 16, Pages 1 - 29

https://doi.org/10.1145/3469856

Published: 08 September 2021 Publication History

Abstract

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.

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Cited By

Liu YZhang RGuo Jde Rijke MNejdl WAuer SKarras OCha MMoens MNajork M(2025)Robust Information RetrievalProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703476(1008-1011)Online publication date: 10-Mar-2025
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Liu YZhang RGuo Jde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Robust Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661380(3009-3012)Online publication date: 10-Jul-2024
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Index Terms

Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 1

January 2022

599 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3483337

Editor:
Min Zhang
Tsinghua University, China

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 08 September 2021

Accepted: 01 June 2021

Revised: 01 March 2021

Received: 01 September 2020

Published in TOIS Volume 40, Issue 1

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HKRGC GRF
HKU-TCL Joint Research Centre for Artificial Intelligence
National Natural Science Foundation of China (NSFC)
Key-Area Research and Development Program of Guangdong Province

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https://dl.acm.org/doi/10.1145/3701551.3703476
Li SYuan GYang MShen YLi CXu RZhao X(2024)Improving Semi-Supervised Text Classification with Dual Meta-LearningACM Transactions on Information Systems10.1145/364861242:4(1-28)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3648612
Liu YZhang RGuo Jde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Robust Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661380(3009-3012)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661380
Zhang HZhang RGuo Jde Rijke MFan YCheng XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Are Large Language Models Good at Utility Judgments?Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657784(1941-1951)Online publication date: 10-Jul-2024
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