@inproceedings{2a108b1d69d0430fbe9aefc2bfebfb78,
title = "A wavelet-based filtering approach to functional bipartite ranking",
abstract = "It is the purpose of this paper to investigate the bipartite ranking task from the perspective of functional data analysis (FDA). Precisely, given a collection of independent copies of a (possibly sampled) random curve X (X(t))t[0,1] taking its values in a function space X, with a locally smooth autocorrelation structure and to which a binary label Y \{1, 1\} is randomly assigned, the goal is to learn a scoring functions: X R with optimal ROC curve. Based on nonlinear wavelet-based approximation, it is shown how to select compact finite dimensional representations of the input curves in order to build accurate ranking rules, using recent advances in the ranking problem for multivariate data with binary feedback.",
keywords = "AUC maximization, ROC optimization, bipartite ranking, filtering methods, functional data analysis, supervised learning, wavelet analysis",
author = "S. Clemencon and M. Depecker",
year = "2011",
month = sep,
day = "5",
doi = "10.1109/SSP.2011.5967819",
language = "English",
isbn = "9781457705700",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
pages = "777--780",
booktitle = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011",
note = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011 ; Conference date: 28-06-2011 Through 30-06-2011",
}