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Metric learning for text documents.

Guy Lebanon1

  • 1Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN 47907, USA. lebanon@stat.purdue.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 29, 2006
PubMed
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This study introduces a novel method for learning data-driven distance metrics, outperforming standard measures in text classification by optimizing Riemannian geometry for machine learning algorithms.

Area of Science:

  • Machine Learning
  • Differential Geometry
  • Statistical Inference

Background:

  • Machine learning algorithms often require effective distance metrics for optimal performance.
  • Default metrics like Euclidean may not capture the underlying data structure.
  • Learning data-specific metrics is crucial for improving algorithm accuracy.

Purpose of the Study:

  • To develop a method for learning Riemannian metrics from data.
  • To apply this method to the multinomial simplex for text classification.
  • To compare the learned metric against existing measures like tfidf cosine similarity.

Main Methods:

  • Learning a parametric family of Riemannian metrics by maximizing the inverse data volume.
  • Utilizing maximum likelihood estimation under a specific probability model.

Related Experiment Videos

  • Applying pull-back metrics of Fisher information under Lie group transformations.

    • Main Results:

    • The proposed method successfully learns a Riemannian metric tailored to the data.
    • The learned geodesic distance measure demonstrated superior performance compared to tfidf cosine similarity in text document classification.
    • The approach provides a statistically grounded method for metric learning.

    Conclusions:

    • Data-driven Riemannian metric learning offers significant advantages over default metrics.
    • The developed technique enhances machine learning model performance, particularly in text analysis.
    • This work bridges differential geometry and machine learning for improved data representation.