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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...
Positive Definite $\ell_1$ Penalized Estimation of Large Covariance Matrices
Alternating direction methods Large covariance matrices Matrix norm Positive-de nite estimation Sparsity Soft-thresholding.
2012/9/18
The thresholding covariance estimator has nice asymptotic properties for estimating sparse large covariance matrices, but it often has negative eigenvalues when used in real data analysis. To simultan...
Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods
Detecting Events Patterns Large-Scale User Generated Textual Streams Statistical Learning Methods
2012/9/18
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is pu...
Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies
Efficient computation a linear mixed model on large-scale data sets applications genetic studies
2012/9/19
Motivated by genome-wide association studies we consider astan-dard linear model with one additional random effect in situations where many predictors have been collected on the same subjects and each...
Model-Based Clustering of Large Networks
social networks stochastic block models finite mixture models EM algorithms generalized EM algorithms variational EM algorithms MM algorithms
2012/9/18
We describe a network clustering framework, based on finite mix-ture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Rela-ti...
Large information plus noise random matrix models and consistent subspace estimation in large sensor networks
Large information plus noise random matrix models consistent subspace estimation large sensor networks
2011/7/7
In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by ...
A Subspace Estimator for Fixed Rank Perturbations of Large Random Matrices
Large Random Matrix Theory MUSIC Algorithm Extreme Eigenvalues
2011/7/6
This paper deals with the problem of parameter estimation based on certain eigenspaces of the empirical covariance matrix of an observed multidimensional time series, in the case where the time series...
Large Vector Auto Regressions
Time Series Vector Auto Regression Regularization Lasso Group Lasso
2011/7/6
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for...
Large Scale Correlation Screening
High dimensional inference Variable selection Phase transition Poisson limit Renyi entropy Thresholding Sparsity False discovery
2011/3/18
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlatio...
Penalized orthogonal-components regression for large p small n data
Empirical Bayes thresholding Latent-variable model Sparse predictors Supervised dimension reduction
2009/9/16
Here we propose a penalized orthogonal-components regression (POCRE) for large $p$ small $n$ data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlati...