Regularization Paths for Generalized Linear Models via Coordinate Descent.

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.

• Publication year

  2010

• Venue

  Journal of Statistical Software

• Publication date

  2010-02-02

• Fields of study

  Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.18637/JSS.V033.I01PMID 20808728PMCID PMC2929880
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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