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Vanessa Gómez-Verdejo - IEEE Xplore Author Profile
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Vanessa Gómez-Verdejo

Also published under: Vanessa Gomez-Verdejo, V. Gomez-Verdejo, V. Gómez-Verdejo

Publications
17
Citations
244
Publications by Year
20032024
Co-Authors:
Show All Co-Authors (25)
Image of author Vanessa Gómez-Verdejo

Vanessa Gómez-Verdejo

Also published under: Vanessa Gomez-Verdejo, V. Gomez-Verdejo, V. Gómez-Verdejo

Affiliation

Department of Signal Processing and Communications
University Carlos III de Madrid
Madrid, Spain

Publication Topics

Biography

Vanessa Gómez-Verdejo was born in Madrid, Spain, in 1979. She received the telecommunication engineering degree from the Universidad Politécnica de Madrid, Madrid, in 2002. She received the Ph.D. degree from the Universidad Carlos III de Madrid, Madrid, in 2007.,She is currently a Visiting Professor in the Department of Signal Theory and Communications, Universidad Carlos III de Madrid. She has co-authored around 20 papers, including journal and conference contributions. She has participated in several research and development projects with public funding and companies, which have provided her with an extensive experience in solving real-world problems. Her current research interests include machine learning algorithms and methods of featur...
Publications
17
Citations
244
Publications by Year
20032024
Co-Authors:
Show All Co-Authors (25)
Author's Published Works

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Adaptive Sparse Gaussian Process

Vanessa Gómez-Verdejo;Emilio Parrado-Hernández;Manel Martínez-Ramón

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 11 | Journal Article |
Cited by: Papers (6)
Adaptive learning is necessary for nonstationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating. Existing solutions only partially cover these needs. Here, we propose the first a...Show More

Adaptive Sparse Gaussian Process

Vanessa Gómez-Verdejo;Emilio Parrado-Hernández;Manel Martínez-Ramón

Year: 2024 | Volume: 35, Issue: 11 | Journal Article |

Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction

Sergio Muñoz-Romero;Jerónimo Arenas-García;Vanessa Gómez-Verdejo

IEEE Transactions on Multimedia
Year: 2018 | Volume: 20, Issue: 7 | Journal Article |
Cited by: Papers (5)
Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and why filters work for audio or visual applications is a desired property, especia...Show More

Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction

Sergio Muñoz-Romero;Jerónimo Arenas-García;Vanessa Gómez-Verdejo

Year: 2018 | Volume: 20, Issue: 7 | Journal Article |

Feature selection in solar radiation prediction using bootstrapped SVRs

O. García-Hinde;V. Gómez-Verdejo;M. Martínez-Ramón;C. Casanova-Mateo;J. Sanz-Justo;S. Jiménez-Fernández;S. Salcedo-Sanz

2016 IEEE Congress on Evolutionary Computation (CEC)
Year: 2016 | Conference Paper |
Cited by: Papers (10)
During the past years solar radiation prediction has become increasingly relevant among the scientific community and Machine Learning techniques have proven to be a useful tool to automatically learn an accurate prediction model. In this paper, we move one step further and try to gain interpretability during the learning process by introducing a novel feature selection approach. Our method trains ...Show More

Feature selection in solar radiation prediction using bootstrapped SVRs

O. García-Hinde;V. Gómez-Verdejo;M. Martínez-Ramón;C. Casanova-Mateo;J. Sanz-Justo;S. Jiménez-Fernández;S. Salcedo-Sanz

Year: 2016 | Conference Paper |

Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection

Sergio Munoz-Romero;Vanessa Gomez-Verdejo;Jeronimo Arenas-Garcia

IEEE Computational Intelligence Magazine
Year: 2016 | Volume: 11, Issue: 4 | Magazine Article |
Cited by: Papers (9)
Multivariate Analysis (MVA) comprises a collection of tools that play a fundamental role in statistical data analysis. These techniques have become increasingly popular since the proposal of Principal Component Analysis (PCA) in 1901 [1]. PCA was proposed as a simple and efficient way to reduce data dimension by projecting the data over the largest variance directions. As illustrated in Fig. 1, PC...Show More

Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection

Sergio Munoz-Romero;Vanessa Gomez-Verdejo;Jeronimo Arenas-Garcia

Year: 2016 | Volume: 11, Issue: 4 | Magazine Article |

Voxel importance in classifier ensembles based on sign consistency patterns: application to sMRI

Vanessa Gomez-Verdejo;Jussi Tohka

2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)
Year: 2016 | Conference Paper |
Cited by: Papers (2)
This paper investigates a new measure of voxel importance based on analysing the sign consistency of voxels in an ensemble of linear SVM classifiers. The ensemble is endowed with a significant degree of diversity since the training set for each individual classifier is a random subsample of the initial training set. The importance of a voxel is proportional to the number of times that the voxel's ...Show More

Voxel importance in classifier ensembles based on sign consistency patterns: application to sMRI

Vanessa Gomez-Verdejo;Jussi Tohka

Year: 2016 | Conference Paper |

Iterative orthonormalized partial least squares with sparsity constraints

Sergio Muñoz-Romero;Jerónimo Arenas-García;Vanessa Gómez-Verdejo

2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2013 | Conference Paper |
Cited by: Papers (2)
Orthonormalized partial least squares (OPLS) is a popular multivariate analysis method to perform supervised feature extraction. In this paper, we propose a novel scheme to solve Orthonormalized Partial Least Squares (OPLS) that can be easily modified to include additional constraints over the input data projection vectors. This scheme is used to implement an OPLSmethod with sparsity constraints (...Show More

Iterative orthonormalized partial least squares with sparsity constraints

Sergio Muñoz-Romero;Jerónimo Arenas-García;Vanessa Gómez-Verdejo

Year: 2013 | Conference Paper |

Real AdaBoost With Gate Controlled Fusion

Efraín Mayhua-Lopez;Vanessa Gomez-Verdejo;Aníbal R. Figueiras-Vidal

IEEE Transactions on Neural Networks and Learning Systems
Year: 2012 | Volume: 23, Issue: 12 | Journal Article |
Cited by: Papers (9)
In this brief, we propose to increase the capabilities of standard real AdaBoost (RAB) architectures by replacing their linear combinations with a fusion controlled by a gate with fixed kernels. Experimental results in a series of well-known benchmark problems support the effectiveness of this approach in improving classification performance. Although the need for cross-validation processes obviou...Show More

Real AdaBoost With Gate Controlled Fusion

Efraín Mayhua-Lopez;Vanessa Gomez-Verdejo;Aníbal R. Figueiras-Vidal

Year: 2012 | Volume: 23, Issue: 12 | Journal Article |

Voxel Selection in MRI through Bagging and Conformal Analysis: Application to Detection of Obsessive Compulsive Disorder

Emilio Parrado-Hernandez;Vanessa Gómez-Verdejo;Manel Martínez-Ramón;John Shawe-Taylor;P. Alonso;J. Pujol;J.M. Menchón;N. Cardoner;Carles Soriano-Mas

2012 Second International Workshop on Pattern Recognition in NeuroImaging
Year: 2012 | Conference Paper |
Cited by: Papers (2)
In this work we apply a multivariate feature selection method based on bagging linear SVMs to construct a classifier able to differentiate among control subjects and patients with obsessive compulsive disorder (OCD). Our method selects sets of voxels that are relevant for the detection of the disease. The voxel selection is completed with a conformal analysis based refinement that controls over fi...Show More

Voxel Selection in MRI through Bagging and Conformal Analysis: Application to Detection of Obsessive Compulsive Disorder

Emilio Parrado-Hernandez;Vanessa Gómez-Verdejo;Manel Martínez-Ramón;John Shawe-Taylor;P. Alonso;J. Pujol;J.M. Menchón;N. Cardoner;Carles Soriano-Mas

Year: 2012 | Conference Paper |

fMRI brain mapping with kernels

Manel Martinez-Ramón;Mariléa de Cassia Gomes-Vilela;Vanessa Gómez-Verdejo;Antonio Oliviero

2012 3rd International Workshop on Cognitive Information Processing (CIP)
Year: 2012 | Conference Paper |
Functional Magnetic Resonance Imaging is a technique for the study of the human brain that can detect the regionally specific effects of brain stimuli or activity through the detection of the activity related BOLD signal. The standard fMRI techniques include the use of the so called General Linear Model (GLM), which assumes that the combination of different activity in the brain present linear beh...Show More

fMRI brain mapping with kernels

Manel Martinez-Ramón;Mariléa de Cassia Gomes-Vilela;Vanessa Gómez-Verdejo;Antonio Oliviero

Year: 2012 | Conference Paper |

Support Vector Machines With Constraints for Sparsity in the Primal Parameters

Vanessa Gomez-Verdejo;Manel Martinez-Ramon;Jerónimo Arenas-Garcia;Miguel Lazaro-Gredilla;Harold Molina-Bulla

IEEE Transactions on Neural Networks
Year: 2011 | Volume: 22, Issue: 8 | Journal Article |
Cited by: Papers (7)
This paper introduces a new support vector machine (SVM) formulation to obtain sparse solutions in the primal SVM parameters, providing a new method for feature selection based on SVMs. This new approach includes additional constraints to the classical ones that drop the weights associated to those features that are likely to be irrelevant. A ν-SVM formulation has been used, where ν indicates the ...Show More

Support Vector Machines With Constraints for Sparsity in the Primal Parameters

Vanessa Gomez-Verdejo;Manel Martinez-Ramon;Jerónimo Arenas-Garcia;Miguel Lazaro-Gredilla;Harold Molina-Bulla

Year: 2011 | Volume: 22, Issue: 8 | Journal Article |

Adaptive One-Class Support Vector Machine

Vanessa Gomez-Verdejo;Jerónimo Arenas-Garcia;Miguel Lazaro-Gredilla;Ángel Navia-Vazquez

IEEE Transactions on Signal Processing
Year: 2011 | Volume: 59, Issue: 6 | Journal Article |
Cited by: Papers (36)
In this correspondence, we derive an online adaptive one-class support vector machine. The machine structure is updated via growing and pruning mechanisms and the weights are updated using structural risk minimization principles underlying support vector machines. Our approach leads to very compact machines compared to other online kernel methods whose size, unless truncated, grows almost linearly...Show More

Adaptive One-Class Support Vector Machine

Vanessa Gomez-Verdejo;Jerónimo Arenas-Garcia;Miguel Lazaro-Gredilla;Ángel Navia-Vazquez

Year: 2011 | Volume: 59, Issue: 6 | Journal Article |

Improving boosting performance with a local combination of learners

Efraín Mayhua-López;Vanessa Gómez-Verdejo;Aníbal R. Figueiras-Vidal

The 2010 International Joint Conference on Neural Networks (IJCNN)
Year: 2010 | Conference Paper |
Cited by: Papers (1)
This work explores the possibility of improving the performance of Real Adaboost ensemble classifiers by replacing their standard linear combination of learners by a gating scheme. This more powerful fusion method is defined following the epoch-by-epoch construction of boosting ensembles. Preliminary experimental results support the potential of this new approach.Show More

Improving boosting performance with a local combination of learners

Efraín Mayhua-López;Vanessa Gómez-Verdejo;Aníbal R. Figueiras-Vidal

Year: 2010 | Conference Paper |

A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles

Vanessa Gomez-Verdejo;JerÓnimo Arenas-Garcia;AnÍbal R. Figueiras-Vidal

IEEE Transactions on Neural Networks
Year: 2008 | Volume: 19, Issue: 1 | Journal Article |
Cited by: Papers (32)
Progressively emphasizing samples that are difficult to classify correctly is the base for the recognized high performance of real Adaboost (RA) ensembles. The corresponding emphasis function can be written as a product of a factor that measures the quadratic error and a factor related to the proximity to the classification border; this fact opens the door to explore the potential advantages provi...Show More

A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles

Vanessa Gomez-Verdejo;JerÓnimo Arenas-Garcia;AnÍbal R. Figueiras-Vidal

Year: 2008 | Volume: 19, Issue: 1 | Journal Article |

Designing RBF classifiers for weighted boosting

V. Gomez-Verdejo;J. Arenas-Garcia;M. Ortega-Moral;A.R. Figueiras-Vidal

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
Year: 2005 | Conference Paper |
Cited by: Papers (2)
The recent interest in combining neural networks has produced a variety of techniques. This paper deals with boosting methods, in particular, real AdaBoost schemes built up with radial basis function networks. Real Adaboost emphasis function can be divided into two different terms, the first only focus on the quadratic error of each pattern and the second only takes into account its "proximity" to...Show More

Designing RBF classifiers for weighted boosting

V. Gomez-Verdejo;J. Arenas-Garcia;M. Ortega-Moral;A.R. Figueiras-Vidal

Year: 2005 | Volume: 2 | Conference Paper |

New algorithms for improved adaptive convex combination of LMS transversal filters

J. Arenas-Garcia;V. Gomez-Verdejo;A.R. Figueiras-Vidal

IEEE Transactions on Instrumentation and Measurement
Year: 2005 | Volume: 54, Issue: 6 | Journal Article |
Cited by: Papers (89)
Among all adaptive filtering algorithms, Widrow and Hoff's least mean square (LMS) has probably become the most popular because of its robustness, good tracking properties and simplicity. A drawback of LMS is that the step size implies a compromise between speed of convergence and final misadjustment. To combine different speed LMS filters serves to alleviate this compromise, as it was demonstrate...Show More

New algorithms for improved adaptive convex combination of LMS transversal filters

J. Arenas-Garcia;V. Gomez-Verdejo;A.R. Figueiras-Vidal

Year: 2005 | Volume: 54, Issue: 6 | Journal Article |

Separate-variable adaptive combination of LMS adaptive filters for plant identification

J. Arenas-Garcia;V. Gomez-Verdejo;M. Martinez-Ramon;A.R. Figueiras-Vidal

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)
Year: 2003 | Conference Paper |
Cited by: Papers (19)
The Least Mean Square (LMS) algorithm has become a very popular algorithm for adaptive filtering due to its robustness and simplicity. An adaptive convex combination of one fast a one slow LMS filters has been previously proposed for plant identification, as a way to break the speed vs precision compromise inherent to LMS filters. In this paper, an improved version of this combination method is pr...Show More

Separate-variable adaptive combination of LMS adaptive filters for plant identification

J. Arenas-Garcia;V. Gomez-Verdejo;M. Martinez-Ramon;A.R. Figueiras-Vidal

Year: 2003 | Conference Paper |

Multiple plant identifier via adaptive LMS convex combination

J. Arenas-Garcia;M. Martinez-Ramon;V. Gomez-Verdejo;A.R. Figueiras-Vidal

IEEE International Symposium on Intelligent Signal Processing, 2003
Year: 2003 | Conference Paper |
Cited by: Papers (13)
The least mean square (LMS) algorithm has become a very popular algorithm for adaptive filtering due to its robustness and simplicity. A difficulty concerning LMS filters is their inherent compromise between tracking capabilities and precision, that is imposed by the selection of a fixed value for the adaption step. An adaptive convex combination of one fast LMS filter (high adaption step) and one...Show More

Multiple plant identifier via adaptive LMS convex combination

J. Arenas-Garcia;M. Martinez-Ramon;V. Gomez-Verdejo;A.R. Figueiras-Vidal

Year: 2003 | Conference Paper |

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