Výsledky vyhledávání - "Gloaguen, Arnaud"

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Sparse Factor Analysis for Categorical Data with the Group-Sparse Generalized Singular Value Decomposition

Autor: Yu, Ju-Chi, Borgne, Julie Le, Krishnan, Anjali, Gloaguen, Arnaud, Yang, Cheng-Ta, Rabin, Laura A, Abdi, Hervé, Guillemot, Vincent

Correspondence analysis, multiple correspondence analysis and their discriminant counterparts (i.e., discriminant simple correspondence analysis and discriminant multiple correspondence analysis) are methods of choice for analyzing multivariate categ

Externí odkaz: http://arxiv.org/abs/2409.11789

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Tensor Generalized Canonical Correlation Analysis

Autor: Girka, Fabien, Gloaguen, Arnaud, Brusquet, Laurent Le, Zujovic, Violetta, Tenenhaus, Arthur

Regularized Generalized Canonical Correlation Analysis (RGCCA) is a general statistical framework for multi-block data analysis. RGCCA enables deciphering relationships between several sets of variables and subsumes many well-known multivariate analy

Externí odkaz: http://arxiv.org/abs/2302.05277

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Akademický článek

Tensor generalized canonical correlation analysis

Autor: Girka, Fabien, Gloaguen, Arnaud, Le Brusquet, Laurent, Zujovic, Violetta, Tenenhaus, Arthur

Publikováno v: In Information Fusion February 2024 102

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Akademický článek

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Multiblock data analysis with the RGCCA package

Autor: Girka, Fabien, Camenen, Etienne, Peltier, Caroline, Gloaguen, Arnaud, Guillemot, Vincent, Le Brusquet, Laurent, Tenenhaus, Arthur

Publikováno v: Journal of Statistical Software
Journal of Statistical Software, 2023, pp.1-36. ⟨10.18637/jss.v000.i00⟩

International audience; Multiblock component methods aim to study the relationships between several sets of variables. Regularized Generalized Canonical Correlation Analysis (RGCCA) is a unified and flexible framework that gathers fifty years of mult

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2100::28bf1ca1cc6931f1098e4f18b45d7e7d
https://hal.inrae.fr/hal-04094025/file/RGCCA.pdf

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Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study

Autor: Mihailov, Angeline, Philippe, Cathy, Gloaguen, Arnaud, Grigis, Antoine, Laidi, Charles, Piguet, Camille, Houenou, Josselin, Frouin, Vincent

Publikováno v: Translational Psychiatry, Vol 10, Iss 1, Pp 1-12 (2020)
Translational Psychiatry

Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::627dbf8108691ab6e6d6fe22f3110956
http://link.springer.com/article/10.1038/s41398-020-00894-3

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Rank-R Multiway Logistic Regression

Autor: Girka, Fabien, Chevaillier, Pierrick, Gloaguen, Arnaud, Gennari, Giulia, Dehaene-Lambertz, Ghislaine, Le Brusquet, Laurent, Tenenhaus, Arthur

Publikováno v: 52èmes Journées de Statistiques de laSociété Française de Statistique (SFdS)
52èmes Journées de Statistique
52èmes Journées de Statistique, 2021, Nice, France

les 52èmes journées de Statistique 2020 sont reportées ! Elles auront lieu du 7 au 11 Juin 2021; International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7b03c995b6da14cd499079ffa7cdb9fc
https://hal-centralesupelec.archives-ouvertes.fr/hal-03051752/file/Girka_2020_JDS_RankR_Logistic_Regression.pdf

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Akademický článek

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Sparse Multiple Correspondence Analysis

Autor: Guillemot, Vincent, Le Borgne, Julie, Gloaguen, Arnaud, Tenenhaus, Arthur, Saporta, Gilbert, Chollet, Sylvie, Beaton, Derek, Abdi, Hervé

Publikováno v: 52èmes Journées de Statistique
52èmes Journées de Statistique, Société Française de Statistique (SFdS), May 2020, Nice, France. pp.830-835

International audience; Multiple Correspondence Analysis (MCA) is the method of choicefor themultivariate analysis of categorical data. In MCA each qualitative variable is representedby a group of binary variables (with a coding scheme called “comp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9842ab182effa07d99003e5ee118e826
https://hal-pasteur.archives-ouvertes.fr/pasteur-03037346/file/Guillemot_2020_JDS_Sparse_MCA.pdf

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Introducing group-sparsity and orthogonality constraints in RGCCA

Autor: Guillemot, Vincent, Gloaguen, Arnaud, Tenenhaus, Arthur, Philippe, Cathy, Abdi, Hervé

Publikováno v: JdS2021 : 52èmes Journées de Statistique
JdS2021 : 52èmes Journées de Statistique, Jun 2021, Nice, France

International audience; RGCCA-a fast and flexible method-generalizes many other well-known methods in order to analyze data-sets comprising multiple blocks of variables. Here we extend RGCCA by adding two new constraints to the RCCCA optimization pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::152184f4e679707c73f5991d3af9e506
https://hal.science/hal-03264640/file/_JdS_2021__Group_SGCCA(1).pdf

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