Classification via local multi-resolution projections

TL;DR

This paper introduces a novel local multi-resolution projection method for binary classification that achieves competitive theoretical performance and improved computational efficiency, especially when the domain is unknown.

Contribution

It proposes a new estimation procedure using localized projections onto a multi-resolution analysis, extending to unknown domains, with theoretical guarantees and computational advantages.

Findings

Estimator performs similarly to local-polynomial estimators in theory.

Method outperforms LPE in computational efficiency due to lattice structure.

Achieves super-fast classification rates under margin assumptions.

Abstract

We focus on the supervised binary classification problem, which consists in guessing the label $Y$ associated to a co-variate $X \in \R^d$, given a set of $n$ independent and identically distributed co-variates and associated labels $(X_i,Y_i)$. We assume that the law of the random vector $(X,Y)$ is unknown and the marginal law of $X$ admits a density supported on a set $\A$. In the particular case of plug-in classifiers, solving the classification problem boils down to the estimation of the regression function $\eta(X) = \Exp[Y|X]$. Assuming first $\A$ to be known, we show how it is possible to construct an estimator of $\eta$ by localized projections onto a multi-resolution analysis (MRA). In a second step, we show how this estimation procedure generalizes to the case where $\A$ is unknown. Interestingly, this novel estimation procedure presents similar theoretical performances as the celebrated local-polynomial estimator (LPE). In addition, it benefits from the lattice structure of the underlying MRA and thus outperforms the LPE from a computational standpoint, which turns out to be a crucial feature in many practical applications. Finally, we prove that the associated plug-in classifier can reach super-fast rates under a margin assumption.