搜索结果: 1-15 共查到“Sparsity”相关记录30条 . 查询时间(0.151 秒)
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/20
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
Enhancing Sparsity by Reweighted l1 Minimization
Iterative reweighting Underdetermined systems of linear equations Compressive sensing Dantzig selector Sparsity
2015/7/9
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
The lasso penalizes a least squares regression by the sum of the absolute values
(L1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many
coefficien...
On Computation of Optimal Controllers Subject to Quadratically Invariant Sparsity Constraints
Quadratically Invariant Sparsity Optimal Controllers
2015/6/19
We consider the problem of constructing optimal sparse controllers. It is known that a property called quadratic invariance of the constraint set is important, and results in the constrained minimum-n...
Problems in Generic Combinatorial Rigidity: Sparsity, Sliders, and Emergence of Components
Algorithms Geometry Graph Theory Matroids Random Graphs Rigidity Theory
2014/12/18
Rigidity theory deals in problems of the following form: given a structure defined by geometric constraints on a set of objects, what information about its geometric behavior is implied by the underly...
International Conference on Optimization, Sparsity and Adaptive Data Analysis
Optimization Sparsity Adaptive Data Analysis
2014/10/20
The purpose of this international conference is to bring experts from different disciplines together to exchange ideas and identify new research opportunities in analyzing large scale data in which sp...
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
expectation propagation neural network multilayer perceptron linear model sparse prior automatic relevance determination
2013/4/28
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation ...
On Sparsity Inducing Regularization Methods for Machine Learning
Sparsity Inducing Regularization Methods for Machine Learning
2013/5/2
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer ...
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...
Nonparametric sparsity and regularization
Sparsity Nonparametrics Variable selection Regularization Proximal meth-ods RKHS
2012/9/17
In this work we are interested in the problems of supervised learning and variable se-lection when the input-output dependence is described by a nonlinear function depending on a few variables. Our go...
Gradually Atom Pruning for Sparse Reconstruction and Extension to Correlated Sparsity
Smoothed l0 l1 minimization compressed sensing reconstruction algorithm correlated sparsity
2012/4/23
We propose a new algorithm for recovery of sparse signals from their compressively sensed samples. The proposed algorithm benefits from the strategy of gradual movement to estimate the positions of no...
On the Role of Diversity in Sparsity Estimation
Role of Diversity Sparsity Estimation Information Theory
2011/9/22
Abstract: A major challenge in sparsity pattern estimation is that small modes are difficult to detect in the presence of noise. This problem is alleviated if one can observe samples from multiple rea...
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
On false discovery rate thresholding for classification under sparsity
false discovery rate thresholding classification under sparsity
2011/7/6
We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The "0"-class (null) is assumed to have a known, symmetric log-concave density while the "1"-c...
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
Multiple Measurement Vectors Block Sparsity Time-Varying Sparsity
2011/6/16
A trend in compressed sensing (CS) is to exploit struc-
ture for improved reconstruction performance. In the
basic CS model (i.e. the single measurement vec-
tor model), exploiting the clustering s...