M
Matthieu Brucher
Researcher at Total S.A.
Publications - 5
Citations - 76883
Matthieu Brucher is an academic researcher from Total S.A.. The author has contributed to research in topics: Nonlinear dimensionality reduction & Manifold alignment. The author has an hindex of 3, co-authored 5 publications receiving 62449 citations. Previous affiliations of Matthieu Brucher include Centre national de la recherche scientifique.
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
A Metric Multidimensional Scaling-Based Nonlinear Manifold Learning Approach for Unsupervised Data Reduction
TL;DR: This work proposes here a nonlinear extension to principal component analysis (PCA) that addresses the projection of data onto the manifold in a Bayesian framework and applies this approach to standard data sets such as the COIL-20 database.
Proceedings ArticleDOI
3D Common-Offset CRS Stack: Simplified Formulation and Application to Foothills Data
Proceedings ArticleDOI
Unsupervised Nonlinear Manifold Learning
TL;DR: The proposed method reduces data considering a unique set of low-dimensional variables and a user-defined cost function in the multidimensional scaling framework and presents an application of the approach to several standard data sets such as the SwissRoll.