Review of sparse methods in regression and classification with application to chemometrics

High-dimensional data often contain many variables that are irrelevant for predicting a response or for an accurate group assignment. The inclusion of such variables in a regression or classification model leads to a loss in performance, even if the contribution of the variables to the model is small. Sparse methods for regression and classification are able to suppress these variables. This is possible by adding an appropriate penalty term to the objective function of the method. An overview of recent sparse methods for regression and classification is provided. The methods are applied to several high-dimensional data sets from chemometrics. A comparison with the non-sparse counterparts allows us to acquire an insight into their performance. Copyright (C) 2012 John Wiley & Sons, Ltd.