搜索结果: 1-14 共查到“统计学其他学科 sparse”相关记录14条 . 查询时间(0.093 秒)
Composite likelihood estimation of sparse Gaussian graphical models with symmetry
Variable selection model selection penalized estimation Gaussian graphical model concentration matrix partial correlation matrix
2012/9/17
In this article, we discuss the composite likelihood estimation of sparse Gaussian graph-ical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the...
Discriminative Sparse Coding on Multi-Manifold for Data Representation and Classification
Discriminative Sparse Coding Multi-Manifold for Data Representation Classification
2012/9/18
Sparse coding has been popularly used as an effective data represen-tation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional s...
Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
Composite likelihood coordinatewise optimization Ising model minorization–maximization principle NP-dimension asymptotic theory HIV drug resistance database.
2012/9/17
The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging, especially in the presence of high-dimensional param...
PAC-Bayesian Estimation and Prediction in Sparse Additive Models
Additive models sparsity regression estimation PAC-Bayesian bounds oracle inequality MCMC stochastic search.
2012/9/17
The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p nparadigm).A PAC-Bayesian strategy is investigated, delivering oracle inequaliti...
Introduction of the so called K-means features caused significant discussion in the deep learning community. Despite their simplicity, these features have achieved state of the art performance on seve...
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Re-Weighted Dynamic Filtering Time-Varying Signal Estimation
2012/9/17
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
Stochastic optimization sparse statistical recovery optimal algorithm high dimensions
2012/9/19
We develop and analyze stochastic optimization algorithms for problems in which the ex-pected loss is strongly convex, and the optimum is (approximately)sparse. Previous approaches are able to exploit...
Structure-Based Bayesian Sparse Reconstruction
Structure-Based Bayesian Sparse Reconstruction
2012/9/19
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the spars...
Detecting sparse cone alternatives for Gaussian random fields, with an application to fMRI
random elds Euler characteristic kinematic formulae volumes of tubes expansion order-restricted inference multivari-ate one-sided hypotheses non-negative least squares.
2012/9/19
Our problem is to nd a good approximation to the P-value of the maximum of a random eld of test statistics for a cone alternative at each point in a sample of Gaussian random elds. These test stati...
Sparse linear (or generalized linear) models combine a standard likelihood func-tion with a sparse prior on the unknown coefficients. These priors can conve-
niently be expressed as a maximization ov...
Sparse Vector Autoregressive Modeling
vector autoregressive (VAR) model sparsity partial spectral coherence (PSC) model selection.
2012/9/18
The vector autoregressive (VAR) model has been widely used for modeling temporal de-pendence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can ...
Classification with minimax fast rates for classes of Bayes rules with sparse representation
Classification Sparsity Decision dyadic trees Minimax rates Aggregation
2009/9/16
We consider the classification problem on the cube $[0,1]^d$ when the Bayes rule is known to belong to some new functions classes. These classes are made of prediction rules satisfying some conditions...
Sparse permutation invariant covariance estimation
Covariance matrix High dimension low sample size large p small n Lasso Sparsity Cholesky decomposition
2009/9/16
The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood approac...
Inferring sparse Gaussian graphical models with latent structure
Gaussian graphical model Mixture model ℓ 1-penalization Model selection Variational inference EM algorithm
2009/9/16
Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional de...