Results 1 - 10 of 18,764

Stochastic Perturbation Theory

by G. W. Stewart , 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
Abstract - Cited by 907 (36 self) - Add to MetaCart
. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating

A Heteroskedasticity-Consistent Covariance Matrix Estimator And A Direct Test For Heteroskedasticity

by Halbert White , 1980
"... This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator ..."
Abstract - Cited by 3211 (5 self) - Add to MetaCart
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator

Bagging predictors

by LEO BREIMAN , 1996
"... Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making ..."
Abstract - Cited by 3650 (1 self) - Add to MetaCart
by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability

On Spectral Clustering: Analysis and an algorithm

by Andrew Y. Ng, Michael I. Jordan, Yair Weiss - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS , 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract - Cited by 1713 (13 self) - Add to MetaCart
in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze

Closed-form solution of absolute orientation using unit quaternions

by Berthold K. P. Horn - J. Opt. Soc. Am. A , 1987
"... Finding the relationship between two coordinate systems using pairs of measurements of the coordinates of a number of points in both systems is a classic photogrammetric task. It finds applications in stereophotogrammetry and in robotics. I present here a closed-form solution to the least-squares pr ..."
Abstract - Cited by 989 (4 self) - Add to MetaCart
. These exact results are to be preferred to approximate methods based on measurements of a few selected points. The unit quaternion representing the best rotation is the eigenvector associated with the most positive eigenvalue of a symmetric 4 X 4 matrix. The elements of this matrix are combinations of sums

A multilinear singular value decomposition

by Lieven De Lathauwer, Bart De Moor, Joos Vandewalle - SIAM J. Matrix Anal. Appl , 2000
"... Abstract. We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, first-order perturbation effects, etc., are ..."
Abstract - Cited by 472 (22 self) - Add to MetaCart
Abstract. We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, first-order perturbation effects, etc

Fivebranes, Membranes And Non-Perturbative String Theory

by Katrin Becker, Melanie Becker, Andrew Strominger , 1995
"... Non-perturbative instanton corrections to the moduli space geometry of type IIA string theory compactified on a Calabi-Yau space are derived and found to contain order e \Gamma1=g s contributions, where g s is the string coupling. The computation reduces to a weighted sum of supersymmetric extrema ..."
Abstract - Cited by 387 (6 self) - Add to MetaCart
Non-perturbative instanton corrections to the moduli space geometry of type IIA string theory compactified on a Calabi-Yau space are derived and found to contain order e \Gamma1=g s contributions, where g s is the string coupling. The computation reduces to a weighted sum of supersymmetric

A rapid hierarchical radiosity algorithm

by Pat Hanrahan, David Salzman - Computer Graphics , 1991
"... This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing lar e polygonal patches. The afgorithm constructs a hierarchic“J representation of the form factor matrix by adaptively subdividing patches into su bpatches according to a user-supplied error bound. The a ..."
Abstract - Cited by 409 (11 self) - Add to MetaCart
. The algorithm guarantees that all form factors are calculated to the same precision, removing many common image artifacts due to inaccurate form factors. More importantly, the al o-rithm decomposes the form factor matrix into at most O? n) blocks (where n is the number of elements). Previous radiosity

Matrix Polynomials

by Peter Lancaster, Panayiotis Psarrakos , 1982
"... Abstract. The pseudospectra of a matrix polynomial P (λ) are sets of complex numbers that are eigenvalues of matrix polynomials which are near to P (λ), i.e., their coefficients are within some fixed magnitude of the coefficients of P (λ). Pseudospectra provide important insights into the sensitivit ..."
Abstract - Cited by 304 (9 self) - Add to MetaCart
into the sensitivity of eigenvalues under perturbations, and have several applications. First, qualitative properties concerning boundedness and connected components of pseudospectra are obtained. Then an accurate continuation algorithm for the numerical determination of the boundary of pseudospectra of matrix

Linear multiuser detectors for synchronous code-division multiple-access channels

by Ruxandra Lupas, Sergio Verdú - IEEE TRANS. INFORM. THEORY , 1989
"... In code-division multiple-access systems, simultaneous mul-tiuser accessing of a common channel is made possible by assigning a signature waveform to each user. Knowledge of these waveforms enables the receiver to demodulate the data streams of each user, upon observation of the sum of the transmitt ..."
Abstract - Cited by 385 (4 self) - Add to MetaCart
of the transmitted signals, perturbed by additive noise. Under the assumptions of symbol-synchronous transmissions and white Gaussian noise, we analyze the detection mechanism at the receiver, comparing different detectors by their bit error rate in the low background noise region, and by their worst-case behavior
Results 1 - 10 of 18,764