搜索结果: 1-11 共查到“Group-Lasso”相关记录11条 . 查询时间(0.054 秒)
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 a ...
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...
Proximal methods for the latent group lasso penalty
Structured sparsity proximal methods regularization
2012/11/23
We consider a regularized least squares problem, with regularization by structured sparsity-inducing norms, which extend the usual $\ell_1$ and the group lasso penalty, by allowing the subsets to over...
Non-asymptotic Oracle Inequalities for the Lasso and Group Lasso in high dimensional logistic model
Logistic model Lasso Group Lasso High-dimensional
2012/6/19
We consider the problem of estimating a function $f_{0}$ in logistic regression model. We propose to estimate this function $f_{0}$ by a sparse approximation build as a linear combinaison of elements ...
基于Group lasso的分布式MIMO雷达参数估计与能量优化
MIMO雷达 压缩感知 线性规划 发射能量优化
2013/8/17
针对压缩感知算法在分布式MIMO雷达参数估计性能上易受噪声影响而出现伪峰、定位不准等问题，结合目标散射系数所满足块稀疏的前提条件，提出了一种基于Group lasso模型框架下压缩感知算法的参数估计。Group lasso作为一种块稀疏模型，可以有效解决感知算法在低SNR时参数估计性能差的问题，有效抑制了噪声对稀疏信号的破坏，其性能明显优于感知算法中常用的凸松弛CVX方法。此外针对MIMO雷达目标...
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...