搜索结果: 1-14 共查到“理论统计学 sparsity”相关记录14条 . 查询时间(2.405 秒)
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/20
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
brain reading structured sparsity convex optimization sparse hierarchical models inter-subject validation proximal methods
2011/6/16
Inverse inference, or “brain reading”, is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some c...
Sparsity considerations for dependent observations
Sparsity considerations dependent observations
2011/3/18
The aim of this paper is to provide a comprehensive introduction for the study of L1-penalized estimators in the context of dependent observations. We define a general $\ell_{1}$-penalized estimator f...
We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. This problem is relevant in machine learning, statistics and s...
Mirror averaging with sparsity priors
Mirror averaging progressive mixture sparsity aggregation of estimators oracleinequalities
2010/3/11
We consider the problem of aggregating the elements of a (possibly infinite) dictionary
for building a decision procedure, that aims at minimizing a given criterion. Along with the
dictionary, an in...
The Bayes oracle and asymptotic optimality of multiple testing procedures under sparsity
Multiple testing FDR Bayes oracle asymptotic optimality
2010/3/10
We investigate the asymptotic optimality of a large class of multiple testing rules using the
framework of Bayesian Decision Theory. We consider a parametric setup, in which observations
come from a...
We empirically investigate the best trade-off between sparse and uniformly-
weighted multiple kernel learning (MKL) using the elastic-net regular-
ization on real and simulated datasets. We find tha...
Learning with Structured Sparsity
Learning Structured Sparsity natural extension standard sparsity concept
2010/3/18
This paper investigates a new learning formulation called structured sparsity, which is a
natural extension of the standard sparsity concept in statistical learning and compressive sensing.By allowin...
This paper develops a theory for group Lasso using a concept called strong group sparsity.
Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals.
This pro...
Taking Advantage of Sparsity in Multi-Task Learning
Advantage Sparsity Multi-Task Learning
2010/3/18
We study the problem of estimating multiple linear regression equations for the purpose
of both prediction and variable selection. Following recent work on multi-task learning
Argyriou et al. [2008]...
The sparsity and bias of the Lasso selection in high-dimensional linear regression
Penalized regression high-dimensional data variable selection bias rate consistency spectral analysis random matrices
2010/4/30
Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436–1462]
showed that, for neighborhood selection in Gaussian graphical models,
under a neighborhood stability condition, the LASSO is consistent,
...
Sparsity oracle inequalities for the Lasso
sparsity oracle inequalities Lasso penalized least squares nonparametric regression dimension reduction
2010/4/29
This paper studies oracle properties of ℓ1-penalized least squares
in nonparametric regression setting with random design. We show that the
penalized least squares estimator satisfies sparsity...
Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting
High-dimensional statistical inference subset selection signal denoising compressivesensing model selection
2010/4/26
The problem of recovering the sparsity pattern of a fixed but unknown vector β ∈ Rp
based on a set of n noisy observations arises in a variety of settings, including subset selection in regression, ...