Model Based Co-Clustering:High Dimension and Estimation Challenges Article Swipe
Le coclustering par modélisation probabiliste, qui peut être vu comme une extension du clustering par modèle de mélange, permet une réduction à la fois du nombre de lignes (individus) et de colonnes (variables) d'un ensemble de données de façon très parcimonieuse tout en préservant l'interprétabilité des données réduites. De plus, il bénéficie de la riche théorie statistique des modèles probabilistes tant pour l’estimation que pour la sélection du modèle. C’est un domaine actif dans lequel de nombreux travaux récents ont produit de nouvelles avancées, d’un point vue théorique comme méthodologique et appliqué. Après une discussion sur ces avancées, deux messages principaux sont développés, étayés par du matériel de recherche spécifique : (1) le co-clustering nécessite des recherches plus approfondies pour résoudre certains problèmes d'estimation bien identifiés, et (2) le co-clustering est une approche très prometteuse pour le clustering dans le cadre de la (très) grande dimension, qui correspond à la tendance mondiale des données modernes. Travail en collaboration avec Christophe Biernacki et Julien Jacques (J Classif 40, 332–381 (2023))
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Model Based Co-Clustering:High Dimension and Estimation ChallengesWork title
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2024Year of publication
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2024-03-11Full publication date if available
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Christine Keribin, Christophe Biernacki, Julien JacquesList of authors in order
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https://inria.hal.science/hal-04862826Publisher landing page
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YesWhether a free full text is available
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