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Fast generalized cross-validation algorithm for sparse model learning.

S Sundararajan1, Shirish Shevade, S Sathiya Keerthi

  • 1Philips Electronics India Ltd., Ulsoor, Bangalore, India. zensid@yahoo.com

Neural Computation
|December 1, 2006
PubMed
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We developed a fast, incremental algorithm for creating sparse linear regression models. This new method optimizes parameters using generalized cross-validation and shows competitive performance against existing techniques.

Area of Science:

  • Machine Learning
  • Statistical Modeling

Background:

  • Linear regression models are fundamental in statistical analysis.
  • Developing efficient algorithms for sparse model design is crucial for handling large datasets.

Purpose of the Study:

  • To introduce a novel, fast, and incremental algorithm for designing sparse linear regression models.
  • To evaluate the performance of the proposed algorithm against established incremental methods.

Main Methods:

  • The algorithm employs generalized cross-validation to optimize multiple smoothing parameters.
  • It iteratively builds a sparse linear regression model.

Main Results:

  • The proposed algorithm generates sparse models efficiently.
  • Performance comparisons on synthetic and real-world data show the algorithm is competitive with existing methods like fast relevance vector machines and orthogonal least squares.

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Conclusions:

  • The developed incremental algorithm offers a competitive and efficient approach for sparse linear regression model design.
  • It provides a valuable alternative for researchers and practitioners working with high-dimensional data.