Examinando por Autor "Camps Valls, Gustavo"

Mostrando 1 - 11 de 11

Bootstrap Feature Selection in Support Vector Machines for Ventricular Fibrillation Detection
(2009-07-23T11:01:23Z) Alonso Atienza, Felipe; Rojo-Álvarez, José Luis; Camps Valls, Gustavo; Rosado Muñoz, A; García Alberola, A
Support Vector Machines (SVM) for classification are being paid special attention in a number of practical applications. When using nonlinear Mercer kernels, the mapping of the input space to a highdimensional feature space makes the input feature selection a difficult task to be addressed. In this paper, we propose the use of nonparametric bootstrap resampling technique to provide with a statistical, distribution independent, criterion for input space feature selection. The confidence interval of the difference of error probability between the complete input space and a reduced-in-one-variable input space, is estimated via bootstrap resampling. Hence, a backward variable elimination procedure can be stated, by removing one variable at each step according to its associated confidence interval. A practical example application to early stage detection of cardiac Ventricular Fibrillation (VF) is presented. Basing on a previous nonlinear analysis based on temporal and spectral VF parameters, we use the SVM with Gaussian kernel and bootstrap resampling to provide with the minimum input space feature set that still holds the classification performance of the complete data. The use of bootstrap resampling is a powerful input feature selection procedure for SVM classifiers.
Kernel Antenna Array Processing
(2007-03-01) Martínez Ramón, Manel; Rojo-Álvarez, José Luis; Camps Valls, Gustavo; Cristodoulou, Christos G
We introduce two support vector machine (SVM)-based approaches for solving antenna problems such as beamforming, sidelobe suppression, and maximization of the signal-to-noise ratio. A basic introduction to SVM optimization is provided and a complex nonlinear SVM formulation developed to handle antenna array processing in space and time. The new optimization formulation is compared with both the minimum mean square error and the minimum variance distortionless response methods. Several examples are included to show the performance of the new approaches.
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data classification and change detection.
(2008-01-01) Camps Valls, Gustavo; Gómez Chova, L; Muñoz Marí, J; Rojo-Álvarez, José Luis; Martínez Ramón, M
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods by formulating them in an adequate high-dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms
Learning Non-linear Time Scales with Kernel Gamma Filters
(2009-07-23T08:47:46Z) Camps Valls, Gustavo; Muñoz Marí, Jordi; Martínez Ramón, Manel; Requena Carrión, Jesús; Rojo-Álvarez, José Luis
Nonlinear System Identification with Composite Relevance Vector Machines
(2007-01-01) Camps Valls, Gustavo; Martínez Ramón, Manel; Rojo-Álvarez, José Luis; Muñoz Marí, Jordi
Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection.
Nonuniform Interpolation of Noisy Signals Using Support Vector Machines
(2007-08-01) Rojo-Álvarez, José Luis; Figuera Pozuelo, Carlos; Camps Valls, Gustavo; Alonso Atienza, Felipe; Martínez Ramón, Manel
The problem of signal interpolation has been intensively studied in the Information Theory literature, in conditions such as unlimited band, nonuniform sampling, and presence of noise. During the last decade, support vector machines (SVM) have been widely used for approximation problems, including function and signal interpolation. However, the signal structure has not always been taken into account in SVM interpolation. We propose the statement of two novel SVM algorithms for signal interpolation, specifically, the primal and the dual signal model based algorithms. Shift-invariant Mercer¿s kernels are used as building blocks, according to the requirement of bandlimited signal. The sinc kernel, which has received little attention in the SVM literature, is used for andlimited reconstruction. Well-known properties of general SVM algorithms (sparseness of the solution, robustness, and regularization) are explored with simulation examples, yielding improved results with respect to standard algorithms, and revealing good characteristics in nonuniform interpolation of noisy signals.
Robust Gamma-filter Using Support Vector Machines
(2009-07-23T09:19:34Z) Camps Valls, Gustavo; Martínez Ramón, Manel; Rojo-Álvarez, José Luis; Soria Olivas, Emilio
This Letter presents a new approach to time-series modelling using the support vector machines (SVM). Although the g-filter can provide stability in several time-series models, the SVM is proposed here to provide robustness in the estimation of the g-filter coefficients. Examples in chaotic time-series prediction and channel equalization show the advantages of the joint SVM g-filter.
Robust Support Vector Regression for Biophysical Variable Estimation from Remotely Sensed Images
(2006-07-01) Camps Valls, Gustavo; Bruzzone, Lorenzo; Rojo-Álvarez, José Luis; Melgani, Farid
This letter introduces the -Huber loss function in the support vector regression (SVR) formulation for the estimation of biophysical parameters extracted from remotely sensed data. This cost function can handle the different types of noise contained in the dataset. The method is successfully compared to other cost functions in the SVR framework, neural networks and classical bio-optical models for the particular case of the estimation of ocean chlorophyll concentration from satellite remote sensing data. The proposed model provides more accurate, less biased, and improved robust estimation results on the considered case study, especially significant when few in situ measurements are available.
Sparse Deconvolution Using Support Vector Machines
(2009-07-23T08:26:56Z) Rojo-Álvarez, José Luis; Martínez Ramón, Manel; Muñoz Marí, Jordi; Camps Valls, Gustavo; M. Cruz, Carlos; Figueiras Vidal, Aníbal R
Support Vector Machines for Nonlinear Kernel ARMA System Identification
(2006-11-01) Martínez Ramón, Manel; Rojo-Álvarez, José Luis; Camps Valls, Gustavo; Muñoz Marí, Jordi; Navia Vázquez, Ángel; Soria Olivas, Emilio; Figueiras Vidal, Aníbal R
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer¿s kernels. This general class can improve model flexibility by emphasizing the input¿output cross information (SVM-ARMA 4k), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA 2k and SVR-ARMA 4k). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems.
Support Vector Machines Framework for Linear Signal Processing
(2009-07-23T09:14:14Z) Rojo-Álvarez, José Luis; Camps Valls, Gustavo; Martínez Ramón, Manel; Soria Olivas, Emilio; Navia Vázquez, Ángel; Figueiras Vidal, Aníbal R
This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM framework.