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A greedy dimension reduction method for classification problems

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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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Dates and versions

hal-02280502 , version 1 (06-09-2019)

Identifiers

  • HAL Id : hal-02280502 , version 1

Cite

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