1.3k citations · 2.4k across the 52 of their papers we have counts for
10 papers · 1 filter
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls
Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and prov…
Information Theory in Density Destructors
J. Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls +2
Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multiva…
Information Theory Measures via Multidimensional Gaussianization
Valero Laparra, J. Emmanuel Johnson, Gustau Camps-Valls +2
Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems. It has several desirable properties for real world applications…
Fair Kernel Learning
Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García +3
New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation…
Sequential Principal Curves Analysis
Valero Laparra, Jesus Malo
This work includes all the technical details of the Sequential Principal Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear and invertible feature extra…
Optimized Kernel Entropy Components
Emma Izquierdo-Verdiguier, Valero Laparra, Robert Jenssen +2
This work addresses two main issues of the standard Kernel Entropy Component Analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gau…
Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis
Valero Laparra, Sandra Jiménez, Gustavo Camps-Valls +1
Mechanisms of human color vision are characterized by two phenomenological aspects: the system is nonlinear and adaptive to changing environments. Conventional attempts to derive t…
Iterative Gaussianization: from ICA to Random Rotations
Valero Laparra, Gustavo Camps-Valls, Jesús Malo
Most signal processing problems involve the challenging task of multidimensional probability density function (PDF) estimation. In this work, we propose a solution to this problem…
Principal Polynomial Analysis
Valero Laparra, Sandra Jiménez, Devis Tuia +2
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principa…
Dimensionality Reduction via Regression in Hyperspectral Imagery
Valero Laparra, Jesus Malo, Gustau Camps-Valls
This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize Prin…