Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification
Myriam Bontonou,
Nicolas Farrugia,
Vincent Gripon
Abstract:It is very common to face classification problems where the number of available labeled samples is small compared to their dimension. These conditions are likely to cause underdetermined settings, with high risk of overfitting. To improve the generalization ability of trained classifiers, common solutions include using priors about the data distribution. Among many options, data structure priors, often represented through graphs, are increasingly popular in the field. In this paper, we introduce a generic mode… Show more
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