Electronic Journal of Statistics

Classification via local multi-resolution projections

Jean-Baptiste Monnier

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We focus on the supervised binary classification problem, which consists in guessing the label Y associated to a co-variate Xd, given a set of n independent and identically distributed co-variates and associated labels (Xi,Yi). 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 ${\mathcal{A}}$. In the particular case of plug-in classifiers, solving the classification problem boils down to the estimation of the regression function $\eta(X)=\mathbb {E}[Y|X]$. Assuming first ${\mathcal{A}}$ to be known, we show how it is possible to construct an estimator of η by localized projections onto a multi-resolution analysis (MRA). In a second step, we show how this estimation procedure generalizes to the case where ${\mathcal{A}}$ 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.

Article information

Electron. J. Statist., Volume 6 (2012), 382-420.

First available in Project Euclid: 19 March 2012

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation 62G08: Nonparametric regression
Secondary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20] 62H12: Estimation

Nonparametric regression random design multi-resolution analysis supervised binary classification margin assumption


Monnier, Jean-Baptiste. Classification via local multi-resolution projections. Electron. J. Statist. 6 (2012), 382--420. doi:10.1214/12-EJS677. https://projecteuclid.org/euclid.ejs/1332162334

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