2015
Nazabal, Alfredo; Artés-Rodríguez, Antonio
Discriminative spectral learning of hidden markov models for human activity recognition Proceedings Article
En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1966–1970, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training
@inproceedings{Nazabal2015,
title = {Discriminative spectral learning of hidden markov models for human activity recognition},
author = {Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178314},
doi = {10.1109/ICASSP.2015.7178314},
isbn = {978-1-4673-6997-8},
year = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1966--1970},
publisher = {IEEE},
address = {Brisbane},
abstract = {Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.},
keywords = {Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.
2013
Alvarez, Mauricio; Luengo, David; Lawrence, Neil D
Linear Latent Force Models Using Gaussian Processes Artículo de revista
En: IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no 11, pp. 2693–2705, 2013.
Resumen | Enlaces | BibTeX | Etiquetas: Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode
@article{Alvarez2013,
title = {Linear Latent Force Models Using Gaussian Processes},
author = {Mauricio Alvarez and David Luengo and Neil D Lawrence},
url = {http://dblp.uni-trier.de/db/journals/pami/pami35.html#AlvarezLL13 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514873},
year = {2013},
date = {2013-01-01},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
volume = {35},
number = {11},
pages = {2693--2705},
abstract = {Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.},
keywords = {Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode},
pubstate = {published},
tppubtype = {article}
}
Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.
2010
Achutegui, Katrin; Rodas, Javier; Escudero, Carlos J; Miguez, Joaquin
A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data Proceedings Article
En: 2010 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–8, IEEE, Zurich, 2010, ISBN: 978-1-4244-5862-2.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, Computational modeling, Data models, generalized IMM system, GIMM approach, indoor radio, Indoor tracking, Kalman filters, maneuvering target motion, Mathematical model, model switching sequential Monte Carlo algorithm, Monte Carlo methods, multipath propagation, multiple model interaction, propagation environment, radio receivers, radio tracking, radio transmitters, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, sensors, state space model, target position dependent data, transmitter-to-receiver distance, wireless technology
@inproceedings{Achutegui2010,
title = {A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data},
author = {Katrin Achutegui and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5648053},
isbn = {978-1-4244-5862-2},
year = {2010},
date = {2010-01-01},
booktitle = {2010 International Conference on Indoor Positioning and Indoor Navigation},
pages = {1--8},
publisher = {IEEE},
address = {Zurich},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Approximation methods, Computational modeling, Data models, generalized IMM system, GIMM approach, indoor radio, Indoor tracking, Kalman filters, maneuvering target motion, Mathematical model, model switching sequential Monte Carlo algorithm, Monte Carlo methods, multipath propagation, multiple model interaction, propagation environment, radio receivers, radio tracking, radio transmitters, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, sensors, state space model, target position dependent data, transmitter-to-receiver distance, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.