CIRM - Videos & books Library - High-dimensional classification by sparse logistic regression

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Auteurs : Abramovich, Felix (Auteur de la Conférence)
CIRM (Editeur )

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Résumé : In this talk we consider high-dimensional classification. We discuss first high-dimensional binary classification by sparse logistic regression, propose a model/feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the non-asymptotic bounds for the resulting misclassification excess risk. Implementation of any complexity penalty-based criterion, however, requires a combinatorial search over all possible models. To find a model selection procedure computationally feasible for high-dimensional data, we consider logistic Lasso and Slope classifiers and show that they also achieve the optimal rate. We extend further the proposed approach to multiclass classification by sparse multinomial logistic regression.

This is joint work with Vadim Grinshtein and Tomer Levy.

Keywords : Complexity penalty; convex relaxation; feature selection; high-dimensionality; minimaxity; misclassification excess risk; sparsity

Codes MSC :
62C20 - Minimax procedures
62H30 - Classification and discrimination; cluster analysis

Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/ABRAMOVICH_Talk.pdf

Informations sur la Vidéo

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Hennenfent, Guillaume

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https://videos.cirm-math.fr/2020-06-03_Abramovitch.mp4

Informations sur la Rencontre

Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2
Organisateurs de la rencontre : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne
Dates : 15/06/2020 - 19/06/2020
Année de la rencontre : 2020
URL Congrès : https://www.cirm-math.com/cirm-virtual-...

Données de citation

DOI : 10.24350/CIRM.V.19640203
Citer cette vidéo: Abramovich, Felix (2020). High-dimensional classification by sparse logistic regression. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19640203
URI : http://dx.doi.org/10.24350/CIRM.V.19640203

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Bibliographie

ABRAMOVICH, Felix, GRINSHTEIN, Vadim, et LEVY, Tomer. Multiclass classification by sparse multinomial logistic regression. arXiv preprint arXiv:2003.01951, 2020. - https://arxiv.org/abs/2003.01951

ABRAMOVICH, Felix et GRINSHTEIN, Vadim. High-dimensional classification by sparse logistic regression. IEEE Transactions on Information Theory, 2018, vol. 65, no 5, p. 3068-3079. - https://doi.org/10.1109/TIT.2018.2884963

ALQUIER, Pierre, COTTET, Vincent, LECUÉ, Guillaume, et al. Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions. The Annals of Statistics, 2019, vol. 47, no 4, p. 2117-2144. - http://dx.doi.org/10.1214/18-AOS1742

BARTLETT, Peter L., JORDAN, Michael I., et MCAULIFFE, Jon D. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 2006, vol. 101, no 473, p. 138-156. - https://www.jstor.org/stable/30047445

BELLEC, Pierre C., LECUÉ, Guillaume, TSYBAKOV, Alexandre B., et al. Slope meets lasso: improved oracle bounds and optimality. The Annals of Statistics, 2018, vol. 46, no 6B, p. 3603-3642. - http://dx.doi.org/10.1214/17-AOS1670

DANIELY, Amit, SABATO, Sivan, BEN-DAVID, Shai, et al. Multiclass learnability and the erm principle. The Journal of Machine Learning Research, 2015, vol. 16, no 1, p. 2377-2404. - http://jmlr.org/papers/volume16/daniely15a/daniely15a.pdf

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