activity
20132026
most citedIterative Gaussianization: from ICA to Random Rotations

120 citations · 353 across the 35 of their papers we have counts for

collaborators
Showing stat.MLShow all

7 papers · 1 filter

stat.ML2020★ 6 cited

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…

stat.ML2020★ 4 cited

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…

stat.ML2016★ 2 cited

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…

stat.ML2016★ 40 cited

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…

stat.ML2016★ 120 cited

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…

stat.ML2016★ 31 cited

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…

stat.ML2016★ 53 cited

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…