Publication | Open Access
Addendum: Regularization and Variable Selection Via the Elastic Net
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2005
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Numerical AnalysisLasso Solution PathsMachine LearningData ScienceData MiningEngineeringLasso Solution PathHigh-dimensional MethodAutomated Machine LearningKnowledge DiscoveryFeature EngineeringParameterized AlgorithmElastic NetComputer ScienceRegularization (Mathematics)Variable SelectionLinear Optimization
We missed an important reference in Section 3.4. In page 309 we stated that ‘. . . which is based on the recently proposed algorithm LARS of Efron et al. (2004). They proved that, starting from zero, the lasso solution paths grow piecewise linearly in a predictable way. They proposed a new algorithm called LARS to solve the entire lasso solution path efficiently by using the same order of computations as a single OLS fit. . . .’ The following sentence should have been included. The piecewise linearity of the lasso solution path was first proved by Osborne et al. (2000), who also described an efficient algorithm for calculating the complete lasso solution path. Reference Osborne, M. R., Presnell, B. and Turlach, B. A. (2000) A new approach to variable selection in least squares problems. IMA J. Numer. Anal., 20, 389–403.
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