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Minipatch Learning as Implicit Ridge-Like Regularization.

Tianyi Yao1, Daniel LeJeune2, Hamid Javadi2

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PubMed
Summary
This summary is machine-generated.

Minipatch Ridge (MPRidge) offers implicit ridge-like regularization for machine learning models. This approach trains on small data subsamples, providing a scalable solution for big data challenges and improving generalization performance.

Keywords:
Ridge-like regularizationensemble learningimplicit regularization

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Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Ridge regularization enhances model generalization by reducing overfitting.
  • Direct training of ridge-regularized models is computationally intensive for large datasets.

Purpose of the Study:

  • To propose Minipatch Ridge (MPRidge), an implicit regularization technique.
  • To address computational challenges in training ridge-regularized models on big data.

Main Methods:

  • MPRidge ensembles coefficients from unregularized learners.
  • Learners are trained on small, random subsamples of data (minipatches) including features and examples.

Main Results:

  • MPRidge demonstrates an implicit ridge-like regularizing effect.
  • Performance is comparable to explicit ridge regularization across various predictors.
  • The method is embarrassingly parallelizable.

Conclusions:

  • MPRidge offers a computationally efficient alternative for ridge-like regularization.
  • It effectively improves generalization in big-data machine learning settings.