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Chebyshev and Fourier Spectral Methods

by John P. Boyd , 1999
"... ..."
Abstract - Cited by 778 (12 self) - Add to MetaCart
Abstract not found

An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

by Ingrid Daubechies, Michel Defrise, Christine De Mol , 2008
"... ..."
Abstract - Cited by 752 (9 self) - Add to MetaCart
Abstract not found

Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise

by Joel A. Tropp , 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
Abstract - Cited by 496 (2 self) - Add to MetaCart
This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination

Bundle Adjustment -- A Modern Synthesis

by Bill Triggs, Philip McLauchlan, Richard Hartley, Andrew Fitzgibbon - VISION ALGORITHMS: THEORY AND PRACTICE, LNCS , 2000
"... This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics c ..."
Abstract - Cited by 555 (12 self) - Add to MetaCart
This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics

Image denoising using a scale mixture of Gaussians in the wavelet domain

by Javier Portilla, Vasily Strela, Martin J. Wainwright, Eero P. Simoncelli - IEEE TRANS IMAGE PROCESSING , 2003
"... We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vecto ..."
Abstract - Cited by 514 (17 self) - Add to MetaCart
coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously

Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones

by Jos F. Sturm , 1998
"... SeDuMi is an add-on for MATLAB, that lets you solve optimization problems with linear, quadratic and semidefiniteness constraints. It is possible to have complex valued data and variables in SeDuMi. Moreover, large scale optimization problems are solved efficiently, by exploiting sparsity. This pape ..."
Abstract - Cited by 1334 (4 self) - Add to MetaCart
SeDuMi is an add-on for MATLAB, that lets you solve optimization problems with linear, quadratic and semidefiniteness constraints. It is possible to have complex valued data and variables in SeDuMi. Moreover, large scale optimization problems are solved efficiently, by exploiting sparsity

Finding community structure in networks using the eigenvectors of matrices

by M. E. J. Newman , 2006
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
Abstract - Cited by 500 (0 self) - Add to MetaCart
number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong

Locally weighted learning

by Christopher G. Atkeson, Andrew W. Moore , Stefan Schaal - ARTIFICIAL INTELLIGENCE REVIEW , 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract - Cited by 594 (53 self) - Add to MetaCart
This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias

Wireless sensor networks: a survey

by I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci , 2002
"... This paper describes the concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of fact ..."
Abstract - Cited by 1936 (23 self) - Add to MetaCart
This paper describes the concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review

A Compositional Approach to Performance Modelling

by Jane Hillston , 1996
"... Performance modelling is concerned with the capture and analysis of the dynamic behaviour of computer and communication systems. The size and complexity of many modern systems result in large, complex models. A compositional approach decomposes the system into subsystems that are smaller and more ea ..."
Abstract - Cited by 746 (102 self) - Add to MetaCart
Performance modelling is concerned with the capture and analysis of the dynamic behaviour of computer and communication systems. The size and complexity of many modern systems result in large, complex models. A compositional approach decomposes the system into subsystems that are smaller and more
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