786 citations · 5.5k across the 92 of their papers we have counts for
17 papers · 1 filter
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
Daniel Heestermans Svendsen, Maria Piles, Jordi Muñoz-Marí +3
The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the dom…
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…
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls +1
Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive…
Joint Gaussian Processes for Biophysical Parameter Retrieval
Daniel Heestermans Svendsen, Luca Martino, Manuel Campos-Taberner +2
Solving inverse problems is central to geosciences and remote sensing. Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in rem…
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…
Sensitivity Maps of the Hilbert-Schmidt Independence Criterion
Adrián Pérez-Suay, Gustau Camps-Valls
Kernel dependence measures yield accurate estimates of nonlinear relations between random variables, and they are also endorsed with solid theoretical properties and convergence ra…
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…
Kernel Manifold Alignment
Devis Tuia, Gustau Camps-Valls
We introduce a kernel method for manifold alignment (KEMA) and domain adaptation that can match an arbitrary number of data sources without needing corresponding pairs, just few la…
A Unified SVM Framework for Signal Estimation
José Luis Rojo-Álvarez, Manel Martínez-Ramón, Jordi Muñoz-Marí +1
This paper presents a unified framework to tackle estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use of SVMs in estimation problem…
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Jerónimo Arenas-García, Kaare Brandt Petersen, Gustavo Camps-Valls +1
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is incr…