47 search results - page 4 / 10 » Adaptive Sampling and Fast Low-Rank Matrix Approximation |
ICASSP
2011
IEEE
12 years 11 months ago
2011
IEEE
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
PCI
2001
Springer
14 years 7 days ago
2001
Springer
We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values of a matrix A, is not only of theoretical interest but also a fast, v...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications. We discuss two re...
SODA
2008
ACM
13 years 9 months ago
2008
ACM
Approximation of the permanent of a matrix with nonnegative entries is a well studied problem. The most successful approach to date for general matrices uses Markov chains to appr...
SIGPRO
2010
13 years 6 months ago
2010
The recovery of signal parameters from noisy sampled data is a fundamental problem in digital signal processing. In this paper, we consider the following spectral analysis problem...