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Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relativ...
Sharp Variable Selection of a Sparse Submatrix in a High-Dimensional Noisy Matrix
estimation minimax testing random matrices selection of sparse signal sharp selection bounds variable selection
2013/4/28
We observe a $N\times M$ matrix of independent, identically distributed Gaussian random variables which are centered except for elements of some submatrix of size $n\times m$ where the mean is larger ...
Generalized Measures for the Evaluation of Community Detection Methods
Complex Networks Community Detection Evaluation Measure Cluster Analysis Purity Adjusted Rand Index Normalized Mutual Information
2013/5/2
Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating ...
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Sparse approximation Regularization Fused-LASSO Split Bregman Multidimensional signals
2013/5/2
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization prob...
Adding a systematic uncertainty to the signal estimation in the on/off-zone measurements
Adding a systematic uncertainty the signal estimation in the on/off-zone measurements
2013/4/28
The measurements with the background estimation from an off-zone are widely used in astrophysics, accelerator physics and other areas. Usually, the expected number of the background events in the off-...
Node-Based Learning of Multiple Gaussian Graphical Models
graphical models structured sparsity alternating direction method of multipliers gene regulatory networks lasso multivariate normal
2013/4/28
We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of ...
We propose a general Bayesian network model for application in a wide class of problems of therapy monitoring. We discuss the use of stochastic simulation as a computational approach to inference on t...
Compressive Shift Retrieval
Compressed sensing shift retrieval sig-nal reconstruction signal registration
2013/5/2
The classical shift retrieval problem considers two signals in vector form that are related by a cyclic shift. In this paper, we develop a compressive variant where the measurement of the signals is u...
The RAppArmor Package: Enforcing Security Policies in R Using Dynamic Sandboxing on Linux
R Security Linux Sandbox AppArmor
2013/5/2
With the increasing availability of public cloud computing facilities and scientific super computers, there is a great potential for making R available through public or shared resources. This allows ...
Performance of the stochastic MV-PURE estimator in highly noisy settings
robust linear estimation reduced-rank estimation stochastic MV-PURE estimator array signal processing
2013/4/28
The stochastic MV-PURE estimator has been developed to provide linear estimation robust to ill-conditioning, high noise levels, and imperfections in model knowledge. In this paper, we investigate the ...
Object Oriented Data Analysis of Cell-Well Structured Data
data objects cell con uence bright
2013/4/28
Object oriented data analysis (OODA) aims at statistically analyzing populations of complicated objects. This paper is motivated by a study of cell images in cell culture biology, which highlights a c...
On the sphericity test with large-dimensional observations
Large-dimensional data Sphericity test John’stest CLT for linear spectral statistics Large-dimensional sample covariance matrix
2013/4/27
In this paper, we propose corrections to LRT and John's test for sphericity in large-dimension. New formula for the limiting parameters in the CLT for linear spectral statistics of sample covariance m...
Regression with Distance Matrices
functional data analysis mixed data multidimensional scaling shape correlation ma-trix
2013/4/27
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these...
Topic Discovery through Data Dependent and Random Projections
Topic Discovery through Data Dependent and Random Projections
2013/4/27
We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a cond...
Variational Semi-blind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM
Variational Bayesian inference posterior image distribution image reconstruction hyperparameter estimation MRFM experiment
2013/5/2
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind d...