Fast sparse regression and classification
AbstractMany present day applications of statistical learning involve large numbers of predictor variables. Often, that number is much larger than the number of cases or observations available for training the learning algorithm. In such situations, traditional methods fail. Recently, new techniques have been developed, based on regularization, which can often produce accurate models in these settings. This paper describes the basic principles underlying the method of regularization, then focuses on those methods which exploit the sparsity of the predicting model. The potential merits of these methods are then explored by example.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 28 (2012)
Issue (Month): 3 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Regression; Classification; Regularization; Sparsity; Variable selection; Bridge-regression; Lasso; Elastic net; lp-norm penalization;
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