1 citations · 2 across the 5 of their papers we have counts for
6 papers
Bayesian learning of feature spaces for multitasks problems
Carlos Sevilla-Salcedo, Ascensión Gallardo-Antolín, Vanessa Gómez-Verdejo +1
This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) through the exploitation of the Random Fou…
Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space
Alejandro Guerrero-López, Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo +1
Purpose: Handling heterogeneous and mixed data types has become increasingly critical with the exponential growth in real-world databases. While deep generative models attempt to m…
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
Carlos Sevilla-Salcedo, Vandad Imani, Pablo M. Olmos +2
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missin…
Bayesian Sparse Factor Analysis with Kernelized Observations
Carlos Sevilla-Salcedo, Alejandro Guerrero-López, Pablo M. Olmos +1
Multi-view problems can be faced with latent variable models since they are able to find low-dimensional projections that fairly capture the correlations among the multiple views t…
Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis
Carlos Sevilla-Salcedo, Vanessa Gómez-Verdejo, Pablo M. Olmos
The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate s…
Why (and How) Avoid Orthogonal Procrustes in Regularized Multivariate Analysis
Sergio Muñoz-Romero, Vanessa Gómez-Verdejo, Jerónimo Arenas-García
Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction that exploit correlations among input variables of the data representation. One importan…