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Computational Statistics and Data Analysis, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nicolai Meinshausen
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The lasso problem and uniqueness
Electronic Journal of Statistics, 2013 The lasso is a popular tool for sparse linear regression, especially for problems in which the number of variables p exceeds the number of observations n. But when p > n, the lasso criterion is not strictly convex, and hence it may not have a unique minimum. An important question is: when is the lasso solution well-defined (unique)?
Ryan J Tibshirani
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Group lasso with overlap and graph lasso
Proceedings of the 26th Annual International Conference on Machine Learning, 2009We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is typically a union of potentially overlapping groups of co-variates defined a priori, or a set of covariates which tend to be connected to each other when a graph of covariates ...
Laurent Jacob +2 more
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On the “degrees of freedom” of the lasso
Annals of Statistics, 2007 We study the effective degrees of freedom of the lasso in the framework of Stein's unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degrees of freedom of the lasso--a conclusion that requires no special assumption on the predictors.
Hui Zou +2 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible.
Sibao Chen 0001 +3 more
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Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible.
Sibao Chen 0001 +3 more
openaire +1 more source