搜索结果: 1-15 共查到“统计学 sparsity”相关记录23条 . 查询时间(0.072 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...
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/4/28
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...
On Sparsity Inducing Regularization Methods for Machine Learning
Sparsity Inducing Regularization Methods for Machine Learning
2013/5/2
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer ...
Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity
Algorithm Large Multi-task Regression Massive Structured Sparsity
2012/9/17
We develop a highly scalable optimization method called “hierarchical group-thresholding”for solving a multi-task regression model with complex structured sparsity constraints on both input and output...
Nonparametric sparsity and regularization
Sparsity Nonparametrics Variable selection Regularization Proximal meth-ods RKHS
2012/9/17
In this work we are interested in the problems of supervised learning and variable se-lection when the input-output dependence is described by a nonlinear function depending on a few variables. Our go...
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...
On false discovery rate thresholding for classification under sparsity
false discovery rate thresholding classification under sparsity
2011/7/6
We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The "0"-class (null) is assumed to have a known, symmetric log-concave density while the "1"-c...
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
Multiple Measurement Vectors Block Sparsity Time-Varying Sparsity
2011/6/16
A trend in compressed sensing (CS) is to exploit struc-
ture for improved reconstruction performance. In the
basic CS model (i.e. the single measurement vec-
tor model), exploiting the clustering s...
Structured Sparsity via Alternating Directions Methods
structured sparsity overlapping Group Lasso alternating directions methods variable splitting augmented Lagrangian
2011/6/21
We consider a class of sparse learning problems in high dimensional feature space regularized
by a structured sparsity-inducing norm which incorporates prior knowledge of the group
structure of the ...
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...
Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors
Bayesian Sparsity-Path-Analysis Genetic Association Signal
2011/7/5
We explore the use of generalized t priors on regression coefficients to help understand the nature of association signal within "hit regions" of genome-wide association studies.
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...