David Diaz Vico
@uem.es
Profesor Doctor, Escuela de Arquitectura, Ingeniería y Diseño
Universidad Europea de Madrid
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Mathematics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
David Díaz-Vico, Angela Fernández, and José R. Dorronsoro
Springer International Publishing
David Díaz-Vico, Angela Fernández, and José R. Dorronsoro
Springer International Publishing
David Diaz-Vico and Jose R. Dorronsoro
Institute of Electrical and Electronics Engineers (IEEE)
While being one of the first and most elegant tools for dimensionality reduction, Fisher linear discriminant analysis (FLDA) is not currently considered among the top methods for feature extraction or classification. In this paper, we will review two recent approaches to FLDA, namely, least squares Fisher discriminant analysis (LSFDA) and regularized kernel FDA (RKFDA) and propose deep FDA (DFDA), a straightforward nonlinear extension of LSFDA that takes advantage of the recent advances on deep neural networks. We will compare the performance of RKFDA and DFDA on a large number of two-class and multiclass problems, many of them involving class-imbalanced data sets and some having quite large sample sizes; we will use, for this, the areas under the receiver operating characteristics (ROCs) curve of the classifiers considered. As we shall see, the classification performance of both methods is often very similar and particularly good on imbalanced problems, but building DFDA models is considerably much faster than doing so for RKFDA, particularly in problems with quite large sample sizes.
Javier Hernandez-Ortega, Julian Fierrez, Aythami Morales, and David Diaz
IEEE
This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subject's heart rate at each moment. Four alternatives from the literature are tested, three based in hand-crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand-crafted ones. The low error rate achieved by the learning-based model makes possible its application in real scenarios, e.g. in medical or sports environments.
David Díaz-Vico, Jesús Prada, Adil Omari, and José Dorronsoro
IOS Press
Kernel based Support Vector Machines, SVM, one of the most popular machine learning models, usually achieve top performances in two-class classification and regression problems. However, their training cost is at least quadratic on sample size, making them thus unsuitable for large sample problems. However, Deep Neural Networks (DNNs), with a cost linear on sample size, are able to solve big data problems relatively easily. In this work we propose to combine the advanced representations that DNNs can achieve in their last hidden layers with the hinge and ϵ insensitive losses that are used in two-class SVM classification and regression. We can thus have much better scalability while achieving performances comparable to those of SVMs. Moreover, we will also show that the resulting Deep SVM models are competitive with standard DNNs in two-class classification problems but have an edge in regression ones.
David Díaz-Vico, Jesús Prada, Adil Omari, and José R. Dorronsoro
Springer International Publishing
David Diaz-Vico, Anibal R. Figueiras-Vidal, and Jose R. Dorronsoro
IEEE
Abstract–Classification over imbalanced datasets is a highly interesting topic given that many real-world classification problems present a concrete class with a much smaller number of patterns than the others. In this work we shall explore the use of large, fully connected and potentially deep MLPs in such problems. We will consider simple MLPs, with ReLU activations, softmax outputs and categorical cross-entropy loss, showing that, when properly regularized, these relatively straightforward MLP models yield state of the art results in terms of the areas under the ROC curve for both two-class problems (the usual focus in imbalanced classification) as well as for multi-class problems.
David Díaz-Vico, Adil Omari, Alberto Torres-Barrán, and José Ramón Dorronsoro
Springer International Publishing
David Díaz, Alberto Torres, and José R. Dorronsoro
Springer International Publishing