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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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Published on: September 17, 2019

Group Sparse Additive Models.

Junming Yin1, Xi Chen1, Eric P Xing1

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning
|April 11, 2017
PubMed
Summary
This summary is machine-generated.

We introduce GroupSpAM, a new method for sparse variable selection in additive models that leverages covariate structure. GroupSpAM improves upon existing methods for group sparsity in nonparametric settings, enhancing prediction accuracy.

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Sparse variable selection is crucial in high-dimensional data analysis.
  • Existing methods for group sparsity are often limited to parametric models or lack structural information in nonparametric settings.
  • Nonparametric additive models offer flexibility but require specialized variable selection techniques.

Purpose of the Study:

  • To develop a novel method for group sparse variable selection in nonparametric additive models.
  • To incorporate prior knowledge of covariate structure for joint variable selection.
  • To address limitations of existing group lasso and sparse additive models.

Main Methods:

  • Introduced GroupSpAM (Group Sparse Additive Models).
  • Generalized the ℓ1/ℓ2 norm to Hilbert spaces for sparsity-inducing penalties.
  • Derived a novel group-level functional sparsity thresholding condition.
  • Proposed an efficient block coordinate descent algorithm for estimation.

Main Results:

  • GroupSpAM demonstrated superior performance in simulation studies compared to competing methods.
  • The method showed significant improvements in both support recovery and prediction accuracy.
  • Comparative experiments on a real breast cancer dataset validated the practical utility of GroupSpAM.

Conclusions:

  • GroupSpAM effectively handles group sparsity in nonparametric additive models by exploiting covariate structure.
  • The proposed method offers enhanced accuracy and better variable selection performance.
  • GroupSpAM provides a valuable tool for analyzing complex biological and statistical data.