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A greedy dimension reduction method for classification problems

Damiano Lombardi 1 Fabien Raphel 1, 2
1 COMMEDIA - COmputational Mathematics for bio-MEDIcal Applications
Inria de Paris, LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
Abstract : In numerous classification problems, the number of available samples to be used in the classifier training phase is small, and each sample is a vector whose dimension is large. This regime, called high-dimensional/low sample size is particularly challenging when classification tasks have to be performed. To overcome this shortcoming, several dimension reduction methods were proposed. This work investigates a greedy optimisation method that builds a low dimensional classifier input. Some numerical examples are proposed to illustrate the performances of the method and compare it to other dimension reduction strategies.
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Contributor : Damiano Lombardi <>
Submitted on : Friday, September 6, 2019 - 1:46:49 PM
Last modification on : Wednesday, December 9, 2020 - 3:16:38 PM
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  • HAL Id : hal-02280502, version 1


Damiano Lombardi, Fabien Raphel. A greedy dimension reduction method for classification problems. 2019. ⟨hal-02280502⟩



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