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Journal articles

Functional Bipartite Ranking: a Wavelet-Based Filtering Approach

Stéphan Clémençon 1, 2 Marine Depecker 3 
1 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
3 LIMA - Laboratoire Information, Modèles, Apprentissage [Gif-sur-Yvette]
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA). Given a training set of independent realizations of a (possibly sampled) second-order random function with a (locally) smooth autocorrelation structure and to which a binary label is randomly assigned, the objective is to learn a scoring function s with optimal ROC curve. Based on linear/nonlinear wavelet-based approximations, it is shown how to select compact finite dimensional representations of the input curves adaptively, in order to build accurate ranking rules, using recent advances in the ranking problem for multivariate data with binary feedback. Beyond theoretical considerations, the performance of the learning methods for functional bipartite ranking proposed in this paper are illustrated by numerical experiments.
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Submitted on : Friday, September 13, 2019 - 3:46:08 PM
Last modification on : Saturday, June 25, 2022 - 9:12:54 PM


  • HAL Id : hal-02286451, version 1



Stéphan Clémençon, Marine Depecker. Functional Bipartite Ranking: a Wavelet-Based Filtering Approach. Signal Processing, Elsevier, 2013. ⟨hal-02286451⟩



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