The l2,1-Norm Stacked Robust Autoencoders for Domain Adaptation

Authors

  • Wenhao Jiang University of Texas at Arlington
  • Hongchang Gao University of Texas at Arlington
  • Fu-lai Chung Hong Kong Polytechnic University
  • Heng Huang University of Texas at Arlington

DOI:

https://doi.org/10.1609/aaai.v30i1.10274

Keywords:

Autoencoder, Deep Learning, Robust Model

Abstract

Recently, deep learning methods that employ stacked denoising autoencoders (SDAs) have been successfully applied in domain adaptation. Remarkable performance in multi-domain sentiment analysis datasets has been reported, making deep learning a promising approach to domain adaptation problems. SDAs are distinguished by learning robust data representations for recovering the original features that have been artificially corrupted with noise. The idea has been further exploited to marginalize out the random corruptions by a state-of-the-art method called mSDA. In this paper, a deep learning method for domain adaptation called l2,1-norm stacked robust autoencoders (l2,1-SRA) is proposed to learn useful representations for domain adaptation tasks. Each layer of l2,1-SRA contains two steps: a robust linear reconstruction step which is based on l2,1 robust regression and a non-linear squashing transformation step. The experimental results demonstrate that the proposed method is very effective in multiple cross domain classification datasets which include Amazon review dataset, spam dataset from ECML/PKDD discovery challenge 2006 and 20 newsgroups dataset.

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Published

2016-02-21

How to Cite

Jiang, W., Gao, H., Chung, F.- lai, & Huang, H. (2016). The l2,1-Norm Stacked Robust Autoencoders for Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10274

Issue

Section

Technical Papers: Machine Learning Methods