Stacked Robust Adaptively Regularized Auto-Regressions for Domain Adaptation

Wenhao Jiang, Hongchang Gao, Wei Lu, Wei Liu, Fu Lai Korris Chung, Heng Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


Domain adaptation is the situation for supervised learning in which the training data are sampled from the source domain while the test data are sampled from the target domain that follows a different distribution. The key to solving such a problem is to reduce effects of the discrepancy between the training data and test data. Recently, deep learning methods that employ stacked denoising auto-encoders (SDAs) to learn new representations for both domains have been successfully applied in domain adaptation. And, remarkable performance on multi-domain sentiment analysis datasets has been reported, making deep learning a promising approach to domain adaptation problems. In this paper, a deep learning method called Stacked Robust Adaptively Regularized Auto-regressions (SRARAs) is proposed to learn useful representations for domain adaptation problems. Each layer of SRARAs contains two steps: a linear transformation step, which is based on robust adaptively regularized auto-regression, and a non-linear squashing transformation step. The first step aims at reducing the discrepancy between the training data and test data, and the second step is to introduce non-linearity and control the range of the elements in the outputs. The experimental results on text and image datasets demonstrate that the proposed method is very effective.

Original languageEnglish
Article number8360058
Pages (from-to)561-574
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
Publication statusPublished - 1 Mar 2019


  • classification
  • Domain adaptation
  • robust auto-regression

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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