2016
Nazábal, Alfredo; Garcia-Moreno, Pablo; Artés-Rodríguez, Antonio; Ghahramani, Zoubin
Human Activity Recognition by Combining a Small Number of Classifiers. Artículo de revista
En: IEEE journal of biomedical and health informatics, vol. 20, no 5, pp. 1342 – 1351, 2016, ISSN: 2168-2208.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems
@article{Nazabal2016b,
title = {Human Activity Recognition by Combining a Small Number of Classifiers.},
author = {Alfredo Naz\'{a}bal and Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Zoubin Ghahramani},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},
doi = {10.1109/JBHI.2015.2458274},
issn = {2168-2208},
year = {2016},
date = {2016-09-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {20},
number = {5},
pages = {1342 -- 1351},
publisher = {IEEE},
abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},
keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.
Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin
Human Activity Recognition by Combining a Small Number of Classifiers Artículo de revista
En: IEEE journal of biomedical and health informatics, vol. To appear, 2016, ISSN: 2168-2208.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems
@article{Nazabal2016bb,
title = {Human Activity Recognition by Combining a Small Number of Classifiers},
author = {Alfredo Nazabal and Pablo Garcia-Moreno and Antonio Artes-Rodriguez and Zoubin Ghahramani},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},
doi = {10.1109/JBHI.2015.2458274},
issn = {2168-2208},
year = {2016},
date = {2016-01-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {To appear},
publisher = {IEEE},
abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},
keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.
2015
Martino, Luca; Elvira, Victor; Luengo, David; Corander, Jukka
An Adaptive Population Importance Sampler: Learning From Uncertainty Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 63, no 16, pp. 4422–4437, 2015, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks
@article{Martino2015bbb,
title = {An Adaptive Population Importance Sampler: Learning From Uncertainty},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7117437},
doi = {10.1109/TSP.2015.2440215},
issn = {1053-587X},
year = {2015},
date = {2015-08-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {16},
pages = {4422--4437},
publisher = {IEEE},
abstract = {Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.},
keywords = {Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.
2012
Leiva-Murillo, Jose M; Artés-Rodríguez, Antonio
Information-Theoretic Linear Feature Extraction Based on Kernel Density Estimators: A Review Artículo de revista
En: IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no 6, pp. 1180–1189, 2012, ISSN: 1094-6977.
Resumen | Enlaces | BibTeX | Etiquetas: Bandwidth, Density, detection theory, Entropy, Estimation, Feature extraction, Feature extraction (FE), information theoretic linear feature extraction, information theory, information-theoretic learning (ITL), Kernel, Kernel density estimation, kernel density estimators, Machine learning
@article{Leiva-Murillo2012a,
title = {Information-Theoretic Linear Feature Extraction Based on Kernel Density Estimators: A Review},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P44_2012_Information Theoretic Linear Feature Extraction Based on Kernel Density Estimators A Review.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6185689},
issn = {1094-6977},
year = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)},
volume = {42},
number = {6},
pages = {1180--1189},
abstract = {In this paper, we provide a unified study of the application of kernel density estimators to supervised linear feature extraction by means of criteria inspired by information and detection theory. We enrich this study by the incorporation of two novel criteria to the study, i.e., the mutual information and the likelihood ratio test, and perform both a theoretical and an experimental comparison between the new methods and other ones previously described in the literature. The impact of the bandwidth selection of the density estimator in the classification performance is discussed. Some theoretical results that bound classification performance as a function or mutual information are also compiled. A set of experiments on different real-world datasets allows us to perform an empirical comparison of the methods, in terms of both accuracy and computational complexity. We show the suitability of these methods to determine the dimension of the subspace that contains the discriminative information.},
keywords = {Bandwidth, Density, detection theory, Entropy, Estimation, Feature extraction, Feature extraction (FE), information theoretic linear feature extraction, information theory, information-theoretic learning (ITL), Kernel, Kernel density estimation, kernel density estimators, Machine learning},
pubstate = {published},
tppubtype = {article}
}
In this paper, we provide a unified study of the application of kernel density estimators to supervised linear feature extraction by means of criteria inspired by information and detection theory. We enrich this study by the incorporation of two novel criteria to the study, i.e., the mutual information and the likelihood ratio test, and perform both a theoretical and an experimental comparison between the new methods and other ones previously described in the literature. The impact of the bandwidth selection of the density estimator in the classification performance is discussed. Some theoretical results that bound classification performance as a function or mutual information are also compiled. A set of experiments on different real-world datasets allows us to perform an empirical comparison of the methods, in terms of both accuracy and computational complexity. We show the suitability of these methods to determine the dimension of the subspace that contains the discriminative information.
2011
Tuia, D; Verrelst, J; Alonso, L; Perez-Cruz, Fernando; Camps-Valls, Gustavo
Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation Artículo de revista
En: IEEE Geoscience and Remote Sensing Letters, vol. 8, no 4, pp. 804–808, 2011, ISSN: 1545-598X.
Resumen | Enlaces | BibTeX | Etiquetas: Biological system modeling, Biomedical imaging, Biophysical parameter estimation, chlorophyll content estimation, Estimation, fractional vegetation cover, geophysical image processing, hyperspectral compact high-resolution imaging spec, image resolution, leaf area index, model inversion, multioutput support vector regression method, nonparametric biophysical parameter estimation, Parameter estimation, regression, regression analysis, Remote sensing, remote sensing biophysical parameter estimation, remote sensing image, single-output support vector regression method, spectrometers, Support vector machines, support vector regression (SVR), Vegetation mapping
@article{Tuia2011,
title = {Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation},
author = {D Tuia and J Verrelst and L Alonso and Fernando Perez-Cruz and Gustavo Camps-Valls},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5735189},
issn = {1545-598X},
year = {2011},
date = {2011-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {8},
number = {4},
pages = {804--808},
abstract = {This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an $epsilon$-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.},
keywords = {Biological system modeling, Biomedical imaging, Biophysical parameter estimation, chlorophyll content estimation, Estimation, fractional vegetation cover, geophysical image processing, hyperspectral compact high-resolution imaging spec, image resolution, leaf area index, model inversion, multioutput support vector regression method, nonparametric biophysical parameter estimation, Parameter estimation, regression, regression analysis, Remote sensing, remote sensing biophysical parameter estimation, remote sensing image, single-output support vector regression method, spectrometers, Support vector machines, support vector regression (SVR), Vegetation mapping},
pubstate = {published},
tppubtype = {article}
}
This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an $epsilon$-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.