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» A Fast Algorithm for Matrix Balancing
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SADM
2008
178views more  SADM 2008»
13 years 8 months ago
Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem
: Nonnegative matrix approximation (NNMA) is a popular matrix decomposition technique that has proven to be useful across a diverse variety of fields with applications ranging from...
Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
STOC
2001
ACM
138views Algorithms» more  STOC 2001»
14 years 9 months ago
Fast computation of low rank matrix
Given a matrix A, it is often desirable to find a good approximation to A that has low rank. We introduce a simple technique for accelerating the computation of such approximation...
Dimitris Achlioptas, Frank McSherry
SIGIR
2009
ACM
14 years 3 months ago
Fast nonparametric matrix factorization for large-scale collaborative filtering
With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in com...
Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gon...
ICANN
2001
Springer
14 years 1 months ago
Fast Curvature Matrix-Vector Products
The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to ...
Nicol N. Schraudolph
SIAMSC
2011
219views more  SIAMSC 2011»
13 years 4 months ago
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...