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Learning on the symmetric group

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Authors : Vert, Jean-Philippe (Author of the conference)
CIRM (Publisher )

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Abstract : 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.

MSC Codes :
62H30 - Classification and discrimination; cluster analysis
62P10 - Applications of statistics to biology and medical sciences
68T05 - Learning and adaptive systems

    Information on the Video

    Film maker : Hennenfent, Guillaume
    Language : English
    Available date : 20/07/17
    Conference Date : 13/07/17
    Subseries : Research talks
    arXiv category : Machine Learning ; Group Theory
    Mathematical Area(s) : Mathematics in Science & Technology ; Computer Science ; Probability & Statistics
    Format : MP4 (.mp4) - HD
    Video Time : 00:36:10
    Targeted Audience : Researchers
    Download : https://videos.cirm-math.fr/2017-07-13_Vert.mp4

Information on the Event

Event Title : Mathematical methods of modern statistics / Méthodes mathématiques en statistiques modernes
Event Organizers : Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne ; Wesolowski, Jacek
Dates : 10/07/17 - 14/07/17
Event Year : 2017
Event URL : http://conferences.cirm-math.fr/1487.html

Citation Data

DOI : 10.24350/CIRM.V.19194703
Cite this video as: Vert, Jean-Philippe (2017). Learning on the symmetric group. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19194703
URI : http://dx.doi.org/10.24350/CIRM.V.19194703

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