Learning on the symmetric group | Vidéo | Carmin.tv

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Learning on the symmetric group

De Jean-Philippe Vert

Apparaît dans la collection : Mathematical methods of modern statistics / Méthodes mathématiques en statistiques modernes

Many data can be represented as rankings or permutations, raising the question of developing machine learning models on the symmetric group. When the number of items in the permutations gets large, manipulating permutations can quickly become computationally intractable. I will discuss two computationally efficient embeddings of the symmetric groups in Euclidean spaces leading to fast machine learning algorithms, and illustrate their relevance on biological applications and image classification.

Informations sur la vidéo

Date de captation 13/07/2017
Date de publication 20/07/2017
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.19194703
Citer cette vidéo VERT, Jean-Philippe (13/07/2017). Learning on the symmetric group. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19194703
URL https://dx.doi.org/10.24350/CIRM.V.19194703

Domaine(s)

Bibliographie

Barlow, R.E., Bartholomew, D.J., Bremner, J.M., & Brunk, H.D. (1972). Statistical inference under order restrictions. The theory and application of isotonic regression. London: John Wiley & Sons - https://zbmath.org/?q=an:0246.62038
Sysoev, O., & Burdakov, O. (2016). A smoothed monotonic regression via l2 regularization. Technical Report LiTH-MAT-R–2016/01–SE, Department of mathematics, Linköping University - http://liu.diva-portal.org/smash/get/diva2:905380/FULLTEXT01.pdf
Tan, A.C., Naiman, D.Q., Xu, L., Winslow, R.L., & and Geman, D. (2005). Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics, 21(20), 3896-3904 - http://dx.doi.org/10.1093/bioinformatics/bti631

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