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The Impact of Regularization on High-dimensional Logistic Regression

Salehi, Fariborz and Abbasi, Ehsan and Hassibi, Babak (2019) The Impact of Regularization on High-dimensional Logistic Regression. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190628-084529981

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Abstract

Logistic regression is commonly used for modeling dichotomous outcomes. In the classical setting, where the number of observations is much larger than the number of parameters, properties of the maximum likelihood estimator in logistic regression are well understood. Recently, Sur and Candes have studied logistic regression in the high-dimensional regime, where the number of observations and parameters are comparable, and show, among other things, that the maximum likelihood estimator is biased. In the high-dimensional regime the underlying parameter vector is often structured (sparse, block-sparse, finite-alphabet, etc.) and so in this paper we study regularized logistic regression (RLR), where a convex regularizer that encourages the desired structure is added to the negative of the log-likelihood function. An advantage of RLR is that it allows parameter recovery even for instances where the (unconstrained) maximum likelihood estimate does not exist. We provide a precise analysis of the performance of RLR via the solution of a system of six nonlinear equations, through which any performance metric of interest (mean, mean-squared error, probability of support recovery, etc.) can be explicitly computed. Our results generalize those of Sur and Candes and we provide a detailed study for the cases of ℓ^2_2-RLR and sparse (ℓ_1-regularized) logistic regression. In both cases, we obtain explicit expressions for various performance metrics and can find the values of the regularizer parameter that optimizes the desired performance. The theory is validated by extensive numerical simulations across a range of parameter values and problem instances.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
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http://arxiv.org/abs/1906.03761arXivDiscussion Paper
Record Number:CaltechAUTHORS:20190628-084529981
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190628-084529981
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96810
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Jun 2019 17:08
Last Modified:28 Jun 2019 17:08

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