搜索结果: 1-15 共查到“数理统计学 sparse”相关记录28条 . 查询时间(0.031 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：A Sparse Expansion of (Deep) Gaussian Processes
深层 高斯过程 稀疏展开
2023/4/18
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/20
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
Asymptotic Minimaxity of False Discovery Rate Thresholding for Sparse Exponential Data
Minimax Decision theory Minimax Bayes estimation
2015/8/21
Control of the False Discovery Rate (FDR) is a recent innovation in multiple hypothesis
testing, allowing the user to limit the fraction of rejected null hypotheses which correspond to
false rejecti...
Principal component models for sparse functional data
Functional data analysis Principal components Mixed effects model Reduced rank estimation Growth curve
2015/8/21
The elements of a multivariate data set are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete m...
Sparse Principal Component Analysis
Arrays Gene expression Lasso/elastic net Multivariate analysis Singular value decomposition Thresholding
2015/8/21
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However,PCA suffers from the fact that each principal component is a linear combination of all the or...
We propose a sketch-based sampling algorithm, which effectively exploits the data sparsity. Sampling methods have become popular in large-scale data mining and information retrieval, where high data s...
Margin-constrained Random Projections And Very Sparse Random Projections
Random Projections Sampling Maximum Likelihood Asymptotic Analysis
2015/8/21
We propose methods for improving both the accuracy and efficiency of random projections, the popular dimension reduction technique in machine learning and data mining, particularly useful for estimati...
There has been considerable interest in random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space. Let A ∈ Rn×D be our n points i...
We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This settin...
Sparse inverse covariance estimation with the lasso
Sparse inverse covariance estimation the lasso
2015/8/21
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm— the ...
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 ...
Applications of the lasso and grouped lasso to the estimation of sparse graphical models
lasso and grouped lasso sparse graphical models
2015/8/21
We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties.We develop efficient algorithms for fitting these models when the numbe...
On minimax estimation of a sparse normal mean vector
nearly black object robustness white noise model
2015/8/20
Mallows has conjectured that among distributions which are Gaussian but
for occasional contamination by additive noise, the one having least Fisher
information has (two-sided) geometric contaminatio...
Minimax Bayes, asymptotic minimax and sparse wavelet priors
Minimax Decision theory Minimax Bayes estimation
2015/8/20
Pinsker(1980) gave a precise asymptotic evaluation of the minimax mean squared
error of estimation of a signal in Gaussian noise when the signal is known a priori
to lie in a compact ellipsoid in Hi...
Principal components analysis (PCA) is a classical method for the reduction of dimensionality of
data in the form of n observations (or cases) of a vector with p variables. Contemporary data sets
of...