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For high dimensional supervised learning problems, often using problem specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effect...
Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...
Group Lasso estimation of high-dimensional covariance matrices
Group Lasso ℓ 1 penalty high-dimensional covariance estimation basis expansion
2010/10/19
In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensiona...
Group-Lasso on Splines for Spectrum Cartography
Sparsity, splines (group-)Lasso field estimation cognitive radio sensing
2010/10/14
The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish...
We consider the group lasso penalty for the linear model. We note that
the standard algorithm for solving the problem assumes that the model
matrices in each group are orthonormal. Here we consider ...
On the asymptotic properties of the group lasso estimator for linear models
Least Squares Sparsity Group-Lasso Model Selection Oracle Inequalities Persistence
2009/9/16
We establish estimation and model selection consistency, prediction and estimation bounds and persistence for the group-lasso estimator and model selector proposed by Yuan and Lin (2006) for least squ...