Sparse, flexible and efficient modeling using L1 regularization

Research output: Contribution to journalArticlepeer-review


We consider the generic regularized optimization problem ŵ(λ) = arg minw, Σk=1m L(yk, x kTw) + λJ(w). We derive a general characterization of the properties of (loss L, penalty J) pairs which give piecewise linear coefficient paths. Such pairs allow us to efficiently generate the full regularized coefficient paths. We illustrate how we can use our results to build robust, efficient and adaptable modeling tools.

Original languageEnglish
Pages (from-to)375-394
Number of pages20
JournalStudies in Fuzziness and Soft Computing
StatePublished - 2006
Externally publishedYes


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