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Cross-Modal Multivariate Pattern Analysis
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Published on: November 9, 2011

Evolutionary cross-domain discriminative Hessian Eigenmaps.

Si Si1, Dacheng Tao, Kwok-Ping Chan

  • 1Department of Computer Science, The University of Hong Kong, Hong Kong. ssi@cs.hku.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 6, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces cross-domain discriminative Hessian Eigenmaps (CDHE) for machine learning tasks with limited labeled data. CDHE enables effective dimension reduction and class separation across different domains, improving computer vision applications.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Cross-domain learning is crucial for leveraging labeled data in new domains.
  • Existing methods lack effective dimension reduction for cross-domain tasks.
  • Dimension reduction is vital for computer vision tasks like face recognition.

Purpose of the Study:

  • To propose a novel cross-domain dimension reduction technique.
  • To enable learning with disparate labeled and unlabeled datasets.
  • To enhance computer vision applications through effective cross-domain analysis.

Main Methods:

  • Cross-domain discriminative Hessian Eigenmaps (CDHE) is proposed.
  • CDHE minimizes distribution distance between training and test sets.
  • Margin maximization and local geometry preservation are incorporated.

Main Results:

  • CDHE effectively performs cross-domain dimension reduction.
  • The method preserves discriminative and local geometric information.
  • An evolutionary search strategy improves optimization of non-convex objective functions.

Conclusions:

  • CDHE offers a robust solution for cross-domain dimension reduction.
  • The approach is effective for web image annotation and face recognition.
  • CDHE demonstrates potential for learning with limited cross-domain labeled data.