搜索结果: 1-13 共查到“统计核算理论 Sparse”相关记录13条 . 查询时间(0.156 秒)
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...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Sparse approximations in spatio-temporal point-process models
latent Gaussian models linear dynamical systems log Gaussian Cox process approximate inference expectation propagation sparse inference
2013/6/14
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
Guaranteed Sparse Recovery under Linear Transformation
Guaranteed Sparse Recovery Linear Transformation
2013/6/13
We consider the following signal recovery problem: given a measurement matrix $\Phi\in \mathbb{R}^{n\times p}$ and a noisy observation vector $c\in \mathbb{R}^{n}$ constructed from $c = \Phi\theta^* +...
A least-squares method for sparse low rank approximation of multivariate functions
least-squares method sparse low rank approximation multivariate functions
2013/6/14
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regul...
Sparse approximation and recovery by greedy algorithms in Banach spaces
Sparse approximation recovery greedy algorithms Banach spaces
2013/4/28
We study sparse approximation by greedy algorithms. We prove the Lebesgue-type inequalities for the Weak Chebyshev Greedy Algorithm (WCGA), a generalization of the Weak Orthogonal Matching Pursuit to ...
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
Optimization viewpoint Kalman smoothing applications robust sparse estimation
2013/4/28
In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate ...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models
Sure independence screening Variable selection Sparsity Conditional permutation False posi-tive rates
2013/4/27
The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is big...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models
Sure independence screening Variable selection Sparsity Conditional permutation False posi-tive rates
2013/4/27
The varying-coefficient model is an important nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is big...
We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of specia...
Sparse linear discriminant analysis by thresholding for high dimensional data
Classification high dimensionality misclassification rate nor-mality optimal classification rule sparse estimates
2011/6/20
In many social, economical, biological and medical studies, one
objective is to classify a subject into one of several classes based on
a set of variables observed from the subject. Because the prob...
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns ...