Structured variable selection and estimation
نویسندگان
چکیده
منابع مشابه
Structured Variable Selection and Estimation
In linear regression problems with related predictors, it is desirable to do variable selection and estimation by maintaining the hierarchical or structural relationships among predictors. In this paper, we propose nonnegative garrote methods that can naturally incorporate such relationships defined through effect heredity principles or marginality principles. We show that the methods are very ...
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We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsity-inducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual l1-norm and the group l1-norm by allowing the subsets to overlap. This leads to a specific set of allowed nonzero patterns for the solutions of such problem...
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When applying the support vector machine (SVM) to highdimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The lasso and other variable selection techniques have been successfully used in the SVM to perform automatic variable selection. In some problems, there is a natural hierarchical structure among the va...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2009
ISSN: 1932-6157
DOI: 10.1214/09-aoas254