2010
Perez-Cruz, Fernando; Kulkarni, S R
Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks Artículo de revista
En: IEEE Signal Processing Letters, vol. 17, no 4, pp. 355–358, 2010, ISSN: 1070-9908.
Resumen | Enlaces | BibTeX | Etiquetas: communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks
@article{Perez-Cruz2010,
title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},
issn = {1070-9908},
year = {2010},
date = {2010-01-01},
journal = {IEEE Signal Processing Letters},
volume = {17},
number = {4},
pages = {355--358},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},
keywords = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
2009
Djuric, Petar M; Bugallo, Monica F; Closas, Pau; Miguez, Joaquin
Measuring the Robustness of Sequential Methods Proceedings Article
En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 29–32, IEEE, Aruba, Dutch Antilles, 2009, ISBN: 978-1-4244-5179-1.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing
@inproceedings{Djuric2009a,
title = {Measuring the Robustness of Sequential Methods},
author = {Petar M Djuric and Monica F Bugallo and Pau Closas and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5413275},
isbn = {978-1-4244-5179-1},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {29--32},
publisher = {IEEE},
address = {Aruba, Dutch Antilles},
abstract = {Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.},
keywords = {Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing},
pubstate = {published},
tppubtype = {inproceedings}
}