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Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local

Sheng Chen1, Xia Hong, Chris J Harris

  • 1School of Electronics and Computer Science, University of Southampton, Southampton SO17 IBJ, UK. sqc@ecs.soton.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 7, 2004
PubMed
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This study introduces an efficient, automatic algorithm for sparse kernel density estimation, optimizing generalization without user-defined parameters. It achieves comparable accuracy and improved sparsity over existing methods like Support Vector Machines (SVM).

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Data Mining

Background:

  • Kernel density estimation (KDE) is crucial for non-parametric probability density estimation.
  • Existing KDE methods, such as Support Vector Machine (SVM)-based approaches, often require manual parameter tuning.
  • Achieving sparsity in KDE is desirable for computational efficiency and model interpretability.

Purpose of the Study:

  • To develop an efficient and automatic algorithm for constructing sparse kernel density estimates.
  • To optimize model generalization capability directly during the density construction process.
  • To compare the proposed method against state-of-the-art techniques, including SVM-based KDE.

Main Methods:

  • Utilizes an orthogonal forward regression approach for computational efficiency.

Related Experiment Videos

  • Incorporates a local regularization method to enforce sparsity.
  • Employs an incremental minimization of the leave-one-out test score for optimization.

    • Main Results:

    • The proposed algorithm automatically constructs very sparse kernel density estimates.
    • Achieves comparable accuracy to full sample optimized Parzen window density estimates.
    • Outperforms the SVM method in terms of both test accuracy and sparsity.

    Conclusions:

    • The developed algorithm offers an efficient, automatic, and effective solution for sparse kernel density estimation.
    • It provides a competitive alternative to existing methods, particularly SVM-based KDE.
    • The automatic nature and superior performance highlight its practical utility in data analysis.