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Higher Criticism Thresholding: Optimal Feature Selection when Useful Features are Rare and Weak
Criticism Thresholding Feature Selection
2015/8/21
Linear classi cation analysis is a fundamental tool for science
and technology. In important application elds today { genomics and proteomics are examples { one automatically obtains very high-dimen...
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...
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso
sparse inverse covariance selection sparsity graphical lasso Gaussian graphical models graph connected components concentration graph large scale covariance estimation
2015/8/21
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sampl...
Given data from a spherical Gaussian distribution with unknown mean
vector θ, estimates of quadratic functionals are constructed by thresholding. Mean
squared error bounds are derived via a comparis...
Density estimation is a commonly used test case for non-parametric estimation
methods. We explore the asymptotic properties of estimators based on thresholding of
empirical wavelet coecients. Minim...
Neo-Classical Minimax Problems, Thresholding, and Adaptation
Minimax Estimation Adaptive Estimation
2015/8/20
We study the problem of estimating from data Y N(; 2
) under squared-error loss.
We de ne three new scalar minimax problems in which the risk is weighted by the size of .
Simple thresholding...
Automatic thresholding for edge detection in sar imagery
SAR Imagery Edge Detection Thresholding Ratio of Averages
2015/8/20
A few edge detectors are derived from the contrast ratio edge detector to extract linear features from SAR imagery with a constant
probability of false alarm. But all of these detectors need one or m...
A SINGULAR VALUE THRESHOLDING ALGORITHM FOR MATRIX COMPLETION
Nuclear norm minimization matrix completion singular value thresholding Lagrange dual function Uzawa’s algorithm and linearized Bregman iteration
2015/6/17
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxat...
Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators
Singular value thresholding Stein’s unbiased risk estimate (SURE) differentiability of eigenvalues and eigenvectors magnetic resonance cardiac imaging
2015/6/17
In an increasing number of applications, it is of interest to recover an approximately low-rank data matrix from noisy observations. This paper develops an unbiased risk estimate—holding in a Gaussian...
Comparison of automatic and interactive thresholding of hemispherical photography
image analysis LIA for Win32 light environment operator bias operator variation
2015/3/27
This study presents the effects of operator bias and variation in interactive thresholding on the estimation of light environment using hemispherical photography. Twenty-one hemispherical photographs ...
Noise Filtering of Remotely Sensed Images using Iterative Thresholding of Wavelet and Curvelet Transforms
Multi-resolution analysis wavelet transform curvelet transform additive noise noise filtering fixed thresholding iterative thresholding
2014/12/4
This article presents techniques for noise filtering of remotely sensed images based on Multi-resolution Analysis (MRA). Multiresolution techniques provide a coarse-to-fine and scale-invariant decompo...
Statistical Significance of Clustering using Soft Thresholding
Covariance Estimation High Dimension Invariance Principles Unsupervised Learning
2013/6/14
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as...
We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions, and their orientati...
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
A General Iterative Shrinkage Thresholding Algorithm Non-convex Regularized Optimization Problems
2013/5/2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterp...
Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...