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

Virtualconference

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

Loading the player...

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.

Mots-Clés : 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

Réalisateur :

Hennenfent, Guillaume

Langue :

Date de Publication :

Date de Captation :

Sous Collection :

Catégorie arXiv :

Domaine(s) :

Format :

Durée :

Audience :

Download :

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 de la Rencontre : 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

Voir Aussi

[Virtualconference] Experimenting in equilibrium / Auteur de la conférence Wager, Stefan.
[Virtualconference] Structure learning for CTBN's / Auteur de la conférence Miasojedow, Błażej.
[Virtualconference] The price of competition: effect size heterogeneity matters in high dimensions! / Auteur de la conférence Wang, Hua.
[Virtualconference] Scaling of scoring rules / Auteur de la conférence Wallin, Jonas.
[Virtualconference] Hierarchical bayes modeling for large-scale inference / Auteur de la conférence Yekutieli, Daniel.
[Virtualconference] Change: detection, estimation, segmentation / Auteur de la conférence Siegmund, David.
[Virtualconference] High-dimensional, multiscale online changepoint detection / Auteur de la conférence Samworth, Richard.
[Virtualconference] The smoothed multivariate square-root Lasso: an optimization lens on concomitant estimation / Auteur de la conférence Salmon, Joseph.
[Virtualconference] Knockoff genotypes: value in counterfeit / Auteur de la conférence Sabatti, Chiara.
[Virtualconference] Optimal and maximin procedures for multiple testing problems / Auteur de la conférence Rosset, Saharon.
[Virtualconference] Sparse multiple testing: can one estimate the null distribution ? / Auteur de la conférence Roquain, Etienne.
[Virtualconference] Bayesian spatial adaptation / Auteur de la conférence Rockova, Veronika.
[Virtualconference] Universal inference using the split likelihood ratio test / Auteur de la conférence Ramdas, Aaditya K..
[Virtualconference] How to estimate a density on a spider web ? / Auteur de la conférence Picard, Dominique.
[Virtualconference] Post hoc bounds on false positives using reference families / Auteur de la conférence Neuvial, Pierre.
[Virtualconference] Quasi logistic distributions and Gaussian scale mixing / Auteur de la conférence Letac, Gerard.
[Virtualconference] Shrinkage estimation of mean for complex multivariate normal distribution with unknown covariance when p > n / Auteur de la conférence Konno, Yoshihiko.
[Virtualconference] Treatment effect estimation with missing attributes / Auteur de la conférence Josse, Julie.
[Virtualconference] Floodgate: inference for model-free variable importance / Auteur de la conférence Janson, Lucas.
[Virtualconference] On Cholesky structures on real symmetric matrices and their applications / Auteur de la conférence Ishi, Hideyuki.
[Virtualconference] Optimal control of false discovery criteria in the general two-group model / Auteur de la conférence Heller, Ruth.
[Virtualconference] Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so! / Auteur de la conférence Gneiting, Tilmann.
[Virtualconference] De-biasing arbitrary convex regularizers and asymptotic normality / Auteur de la conférence Bellec, Pierre C..
[Virtualconference] Consistent model selection criteria and goodness-of-fit test for common time series models / Auteur de la conférence Bardet, Jean-Marc.

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

Abramovich.jpg

Sélection Signaler une erreur

TUTELLES

PARTENAIRES

Destination de la recherche

Raccourcis

Voir Aussi