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Discovering low-rank shared concept space for adapting text mining models.

Bo Chen1, Wai Lam, Ivor W Tsang

  • 1Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China. bchen@se.cuhk.edu.hk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 20, 2013
PubMed
Summary

This study introduces a novel framework for text mining domain adaptation. It minimizes domain distribution gaps and source domain loss, improving model performance on unlabeled target data.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Domain adaptation is crucial for text mining tasks where labeled source data differs from unlabeled target data.
  • Existing methods often struggle to bridge the distribution gap between domains effectively.

Purpose of the Study:

  • To propose a novel framework for text mining domain adaptation.
  • To discover a low-rank shared concept space that minimizes domain distribution divergence and source domain empirical loss.

Main Methods:

  • The framework discovers a low-rank shared concept space by simultaneously minimizing the distribution gap between source and target domains and the empirical loss on labeled source data.
  • The method is applicable in both the original feature space and the Reproducing Kernel Hilbert Space (RKHS) using kernel tricks.

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  • Theoretical analysis bounds the adaptation model's error based on the embedded distribution gap and source domain empirical loss.

    • Main Results:

    • Extensive experiments on document classification and information extraction tasks demonstrate the framework's efficacy.
    • The proposed method successfully adapts text mining models to target domains with unlabeled data.

    Conclusions:

    • The developed framework offers an effective approach to domain adaptation in text mining.
    • It provides theoretical guarantees for model error bounds, enhancing reliability.