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JSC
2008
162views more  JSC 2008»
13 years 4 months ago
Approximate factorization of multivariate polynomials using singular value decomposition
We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficie...
Erich Kaltofen, John P. May, Zhengfeng Yang, Lihon...
ISSAC
1995
Springer
119views Mathematics» more  ISSAC 1995»
13 years 8 months ago
The Singular Value Decomposition for Polynomial Systems
This paper introduces singular value decomposition (SVD) algorithms for some standard polynomial computations, in the case where the coefficients are inexact or imperfectly known....
Robert M. Corless, Patrizia M. Gianni, Barry M. Tr...
CSDA
2007
128views more  CSDA 2007»
13 years 4 months ago
Regularized linear and kernel redundancy analysis
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures r...
Yoshio Takane, Heungsun Hwang
CVPR
2010
IEEE
13 years 2 months ago
Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the ...
Anders Eriksson, Anton van den Hengel
JC
2008
128views more  JC 2008»
13 years 4 months ago
Lattice rule algorithms for multivariate approximation in the average case setting
We study multivariate approximation for continuous functions in the average case setting. The space of d variate continuous functions is equipped with the zero mean Gaussian measu...
Frances Y. Kuo, Ian H. Sloan, Henryk Wozniakowski