2013
Luengo, David; Via, Javier; Monzon, Sandra; Trigano, Tom; Artés-Rodríguez, Antonio
Cross-Products LASSO Proceedings Article
En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6118–6122, IEEE, Vancouver, 2013, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors
@inproceedings{Luengo2013,
title = {Cross-Products LASSO},
author = {David Luengo and Javier Via and Sandra Monzon and Tom Trigano and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6638840},
issn = {1520-6149},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {6118--6122},
publisher = {IEEE},
address = {Vancouver},
abstract = {Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.},
keywords = {Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
Vila-Forcen, J E; Artés-Rodríguez, Antonio; Garcia-Frias, J
Compressive Sensing Detection of Stochastic Signals Proceedings Article
En: 2008 42nd Annual Conference on Information Sciences and Systems, pp. 956–960, IEEE, Princeton, 2008, ISBN: 978-1-4244-2246-3.
Resumen | Enlaces | BibTeX | Etiquetas: Additive white noise, AWGN, compressive sensing detection, dimensionality reduction techniques, Distortion measurement, Gaussian noise, matrix algebra, Mutual information, optimized projections, projection matrix, signal detection, Signal processing, signal reconstruction, Stochastic processes, stochastic signals, Support vector machine classification, Support vector machines, SVM
@inproceedings{Vila-Forcen2008,
title = {Compressive Sensing Detection of Stochastic Signals},
author = {J E Vila-Forcen and Antonio Art\'{e}s-Rodr\'{i}guez and J Garcia-Frias},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4558656},
isbn = {978-1-4244-2246-3},
year = {2008},
date = {2008-01-01},
booktitle = {2008 42nd Annual Conference on Information Sciences and Systems},
pages = {956--960},
publisher = {IEEE},
address = {Princeton},
abstract = {Inspired by recent work in compressive sensing, we propose a framework for the detection of stochastic signals from optimized projections. In order to generate a good projection matrix, we use dimensionality reduction techniques based on the maximization of the mutual information between the projected signals and their corresponding class labels. In addition, classification techniques based on support vector machines (SVMs) are applied for the final decision process. Simulation results show that the realizations of the stochastic process are detected with higher accuracy and lower complexity than a scheme performing signal reconstruction first, followed by detection based on the reconstructed signal.},
keywords = {Additive white noise, AWGN, compressive sensing detection, dimensionality reduction techniques, Distortion measurement, Gaussian noise, matrix algebra, Mutual information, optimized projections, projection matrix, signal detection, Signal processing, signal reconstruction, Stochastic processes, stochastic signals, Support vector machine classification, Support vector machines, SVM},
pubstate = {published},
tppubtype = {inproceedings}
}