Literature DB >> 36033922

Ridge Regularization: An Essential Concept in Data Science.

Trevor Hastie1.   

Abstract

Ridge or more formally ℓ 2 regularization shows up in many areas of statistics and machine learning. It is one of those essential devices that any good data scientist needs to master for their craft. In this brief ridge fest, I have collected together some of the magic and beauty of ridge that my colleagues and I have encountered over the past 40 years in applied statistics.

Entities:  

Keywords:  Data science; Retrospective; Ridge regression

Year:  2020        PMID: 36033922      PMCID: PMC9410599          DOI: 10.1080/00401706.2020.1791959

Source DB:  PubMed          Journal:  Technometrics        ISSN: 0040-1706


  4 in total

1.  Efficient quadratic regularization for expression arrays.

Authors:  Trevor Hastie; Robert Tibshirani
Journal:  Biostatistics       Date:  2004-07       Impact factor: 5.899

2.  Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares.

Authors:  Trevor Hastie; Rahul Mazumder; Jason D Lee; Reza Zadeh
Journal:  J Mach Learn Res       Date:  2015       Impact factor: 3.654

3.  Reconciling modern machine-learning practice and the classical bias-variance trade-off.

Authors:  Mikhail Belkin; Daniel Hsu; Siyuan Ma; Soumik Mandal
Journal:  Proc Natl Acad Sci U S A       Date:  2019-07-24       Impact factor: 11.205

4.  Learning interactions via hierarchical group-lasso regularization.

Authors:  Michael Lim; Trevor Hastie
Journal:  J Comput Graph Stat       Date:  2015-09-16       Impact factor: 2.302

  4 in total

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