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Fast sparse regression and classification
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DOI：10.1016/j.ijforecast.2012.05.001.png)
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Abstract
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Many 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. (c) 2012 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Regression
Classification
Regularization
Sparsity
Variable selection
Bridge-regression
Lasso
Elastic net
l(p)-norm penalization
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