Metric Learning from Imbalanced Data

Abstract : A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points. Given a task, such a function can be optimized with a metric learning algorithm. Although this research field has received a lot of attention during the past decade, very few approaches have focused on learning a metric in an imbalanced scenario where the number of positive examples is much smaller than the negatives. Here, we address this challenging task by designing a new Mahalanobis metric learning algorithm (IML) which deals with class imbalance. The empirical study performed shows the efficiency of IML.
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Contributor : Léo Gautheron <>
Submitted on : Monday, September 2, 2019 - 2:44:10 PM
Last modification on : Friday, September 6, 2019 - 1:15:41 AM


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  • HAL Id : hal-02276288, version 1
  • ARXIV : 1909.01651


Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban. Metric Learning from Imbalanced Data. 2019. ⟨hal-02276288⟩



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