2015
Elvira, Victor; Martino, Luca; Luengo, David; Bugallo, Monica F
Efficient Multiple Importance Sampling Estimators Artículo de revista
En: IEEE Signal Processing Letters, vol. 22, no 10, pp. 1757–1761, 2015, ISSN: 1070-9908.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation
@article{Elvira2015bb,
title = {Efficient Multiple Importance Sampling Estimators},
author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},
doi = {10.1109/LSP.2015.2432078},
issn = {1070-9908},
year = {2015},
date = {2015-10-01},
journal = {IEEE Signal Processing Letters},
volume = {22},
number = {10},
pages = {1757--1761},
publisher = {IEEE},
abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},
keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},
pubstate = {published},
tppubtype = {article}
}
2012
Garcia-Moreno, Pablo; Artés-Rodríguez, Antonio; Hansen, Lars Kai
A Hold-out Method to Correct PCA Variance Inflation Proceedings Article
En: 2012 3rd International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Baiona, 2012, ISBN: 978-1-4673-1878-5.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, classification scenario, computational complexity, computational cost, Computational efficiency, correction method, hold-out method, hold-out procedure, leave-one-out procedure, LOO method, LOO procedure, Mathematical model, PCA algorithm, PCA variance inflation, Principal component analysis, singular value decomposition, Standards, SVD, Training
@inproceedings{Garcia-Moreno2012,
title = {A Hold-out Method to Correct PCA Variance Inflation},
author = {Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Lars Kai Hansen},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232926},
isbn = {978-1-4673-1878-5},
year = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure was introduced. We propose a Hold-out procedure whose computational cost is lower and, unlike the LOO method, the number of SVD's does not scale with the sample size. We analyze its properties from a theoretical and empirical point of view. Finally we apply it to a real classification scenario.},
keywords = {Approximation methods, classification scenario, computational complexity, computational cost, Computational efficiency, correction method, hold-out method, hold-out procedure, leave-one-out procedure, LOO method, LOO procedure, Mathematical model, PCA algorithm, PCA variance inflation, Principal component analysis, singular value decomposition, Standards, SVD, Training},
pubstate = {published},
tppubtype = {inproceedings}
}