Journal of machine learning research : JMLR最新文献_第3页

Graph estimation from multi-attribute data 基于多属性数据的图估计

Journal of machine learning research : JMLR Pub Date : 2012-10-29 DOI: 10.5555/2627435.2638590

M. Kolar, Han Liu, E. Xing

{"title":"Graph estimation from multi-attribute data","authors":"M. Kolar, Han Liu, E. Xing","doi":"10.5555/2627435.2638590","DOIUrl":"https://doi.org/10.5555/2627435.2638590","url":null,"abstract":"Undirected graphical models are important in a number of modern applications that involve exploring or exploiting dependency structures underlying the data. For example, they are often used to explore complex systems where connections between entities are not well understood, such as in functional brain networks or genetic networks. Existing methods for estimating structure of undirected graphical models focus on scenarios where each node represents a scalar random variable, such as a binary neural activation state or a continuous mRNA abundance measurement, even though in many real world problems, nodes can represent multivariate variables with much richer meanings, such as whole images, text documents, or multi-view feature vectors. In this paper, we propose a new principled framework for estimating the structure of undirected graphical models from such multivariate (or multi-attribute) nodal data. The structure of a graph is inferred through estimation of non-zero partial canonical correlation between nodes. Under a Gaussian model, this strategy is equivalent to estimating conditional independencies between random vectors represented by the nodes and it generalizes the classical problem of covariance selection (Dempster, 1972). We relate the problem of estimating non-zero partial canonical correlations to maximizing a penalized Gaussian likelihood objective and develop a method that efficiently maximizes this objective. Extensive simulation studies demonstrate the effectiveness of the method under various conditions. We provide illustrative applications to uncovering gene regulatory networks from gene and protein profiles, and uncovering brain connectivity graph from positron emission tomography data. Finally, we provide sufficient conditions under which the true graphical structure can be recovered correctly.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122676310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

Network granger causality with inherent grouping structure 网络格兰杰因果关系具有内在的分组结构

Journal of machine learning research : JMLR Pub Date : 2012-10-13 DOI: 10.5555/2789272.2789285

Sumanta Basu, A. Shojaie, G. Michailidis

引用次数: 99

The huge Package for High-dimensional Undirected Graph Estimation in R R语言中高维无向图估计的巨大包

Journal of machine learning research : JMLR Pub Date : 2012-03-01 DOI: 10.5555/2503308.2343681

T. Zhao, Han Liu, K. Roeder, J. Lafferty, L. Wasserman

引用次数: 415

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications 基于绝对惩罚凸极小化的估计与选择及其多阶段自适应应用

Journal of machine learning research : JMLR Pub Date : 2011-12-29 DOI: 10.5555/2503308.2343702

Jian Huang, Cun-Hui Zhang

{"title":"Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications","authors":"Jian Huang, Cun-Hui Zhang","doi":"10.5555/2503308.2343702","DOIUrl":"https://doi.org/10.5555/2503308.2343702","url":null,"abstract":"The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124358544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 56

Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso 大规模图形套索连通分量的精确协方差阈值分割

Journal of machine learning research : JMLR Pub Date : 2011-08-18 DOI: 10.5555/2503308.2188412

R. Mazumder, T. Hastie

引用次数: 222

Spectral Regularization Algorithms for Learning Large Incomplete Matrices 学习大型不完全矩阵的谱正则化算法

Journal of machine learning research : JMLR Pub Date : 2010-03-01 DOI: 10.5555/1756006.1859931

R. Mazumder, T. Hastie, R. Tibshirani

{"title":"Spectral Regularization Algorithms for Learning Large Incomplete Matrices","authors":"R. Mazumder, T. Hastie, R. Tibshirani","doi":"10.5555/1756006.1859931","DOIUrl":"https://doi.org/10.5555/1756006.1859931","url":null,"abstract":"We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 10(6) × 10(6) incomplete matrix with 10(5) observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131620084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1183

Learning Instance-Specific Predictive Models 学习实例特定的预测模型

Journal of machine learning research : JMLR Pub Date : 2010-03-01 DOI: 10.5555/1756006.1953038

S. Visweswaran, G. Cooper

引用次数: 20

Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing 基于变分平均场退火的稀疏图因子模型贝叶斯学习

Journal of machine learning research : JMLR Pub Date : 2010-03-01 DOI: 10.5555/1756006.1859910

Ryo Yoshida, M. West

引用次数: 47

Classification with Incomplete Data Using Dirichlet Process Priors 使用Dirichlet过程先验的不完全数据分类

Journal of machine learning research : JMLR Pub Date : 2010-03-01 DOI: 10.5555/1756006.1953036

Chunping Wang, X. Liao, L. Carin, D. Dunson

引用次数: 27

Ultrahigh Dimensional Feature Selection: Beyond The Linear Model 超高维特征选择:超越线性模型

Journal of machine learning research : JMLR Pub Date : 2009-12-01 DOI: 10.5555/1577069.1755853

Jianqing Fan, R. Samworth, Yichao Wu

{"title":"Ultrahigh Dimensional Feature Selection: Beyond The Linear Model","authors":"Jianqing Fan, R. Samworth, Yichao Wu","doi":"10.5555/1577069.1755853","DOIUrl":"https://doi.org/10.5555/1577069.1755853","url":null,"abstract":"Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Many frequently-used techniques are based on independence screening; examples include correlation ranking (Fan and Lv, 2008) or feature selection using a two-sample t-test in high-dimensional classification (Tibshirani et al., 2003). Within the context of the linear model, Fan and Lv (2008) showed that this simple correlation ranking possesses a sure independence screening property under certain conditions and that its revision, called iteratively sure independent screening (ISIS), is needed when the features are marginally unrelated but jointly related to the response variable. In this paper, we extend ISIS, without explicit definition of residuals, to a general pseudo-likelihood framework, which includes generalized linear models as a special case. Even in the least-squares setting, the new method improves ISIS by allowing feature deletion in the iterative process. Our technique allows us to select important features in high-dimensional classification where the popularly used two-sample t-method fails. A new technique is introduced to reduce the false selection rate in the feature screening stage. Several simulated and two real data examples are presented to illustrate the methodology.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115879336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 424