Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting and Regularization

TL;DR

This paper analyzes binary linear classification under Gaussian mixture models, revealing conditions where overparameterized interpolating classifiers achieve optimal performance and outperform regularized methods, highlighting the importance of SNR and data covariance.

Contribution

It provides new non-asymptotic bounds and conditions for optimality of interpolating classifiers in Gaussian mixture models, extending understanding of overparameterization benefits.

Findings

Interpolating classifiers can achieve asymptotic optimality under certain spectral and SNR conditions.

The results extend to noisy models with label flips, emphasizing SNR's role.

Interpolating estimators can outperform regularized methods under specific data conditions.

Abstract

Deep neural networks generalize well despite being exceedingly overparameterized and being trained without explicit regularization. This curious phenomenon has inspired extensive research activity in establishing its statistical principles: Under what conditions is it observed? How do these depend on the data and on the training algorithm? When does regularization benefit generalization? While such questions remain wide open for deep neural nets, recent works have attempted gaining insights by studying simpler, often linear, models. Our paper contributes to this growing line of work by examining binary linear classification under a generative Gaussian mixture model. Motivated by recent results on the implicit bias of gradient descent, we study both max-margin SVM classifiers (corresponding to logistic loss) and min-norm interpolating classifiers (corresponding to least-squares loss). First, we leverage an idea introduced in [V. Muthukumar et al., arXiv:2005.08054, (2020)] to relate the SVM solution to the min-norm interpolating solution. Second, we derive novel non-asymptotic bounds on the classification error of the latter. Combining the two, we present novel sufficient conditions on the covariance spectrum and on the signal-to-noise ratio (SNR) under which interpolating estimators achieve asymptotically optimal performance as overparameterization increases. Interestingly, our results extend to a noisy model with constant probability noise flips. Contrary to previously studied discriminative data models, our results emphasize the crucial role of the SNR and its interplay with the data covariance. Finally, via a combination of analytical arguments and numerical demonstrations we identify conditions under which the interpolating estimator performs better than corresponding regularized estimates.