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  Computing Leapfrog Regularization Paths with Applications to Large-Scale K-mer Logistic Regression

Benner, P. (2021). Computing Leapfrog Regularization Paths with Applications to Large-Scale K-mer Logistic Regression. Journal of Computational Biology, 2021, 1-10. doi:10.1089/cmb.2020.0284.

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cmb_Benner_2021pdf.pdf (Publisher version), 954KB
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© Philipp Benner 2021

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 Creators:
Benner, Philipp1, Author              
Affiliations:
1Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Free keywords: feature selection,‘1-regularization, LARS, orthogonal matching pursuit
 Abstract: High-dimensional statistics deals with statistical inference when the number of parameters or featurespexceeds the number of observationsn(i.e.,). In this case, the parameter space must be constrained either by regularization or by selecting a small subset offeatures. Feature selection through-regularization combines the benefits of both approaches and has proven to yield good results in practice. However, the functional relation between the regularization strengthand the number of selected featuresmis difficult to determine. Hence, parameters are typically estimated for all possible regularization strengths. These so-called regularization paths can be expensive to compute and most solutions may not even be of interest to the problem at hand. As an alternative, an algorithm is proposed that determines the-regularization strengthiteratively for a fixedm. The algorithm can be used to compute leapfrog regularization paths by subsequently increasingm.

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Language(s): eng - English
 Dates: 2021-03-18
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1089/cmb.2020.0284
 Degree: -

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Title: Journal of Computational Biology
Source Genre: Journal
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Publ. Info: New York, NY : Mary Ann Liebert
Pages: - Volume / Issue: 2021 Sequence Number: - Start / End Page: 1 - 10 Identifier: ISSN: 1066-5277
CoNE: https://pure.mpg.de/cone/journals/resource/954925275499