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Graph Filtering for Improving the Accuracy of Classification problems. (arXiv:2101.04789v1 [stat.ML])

In machine learning, classifiers are typically susceptible to noise in the
training data. In this work, we aim at reducing intra-class noise with the help
of graph filtering to improve the classification performance. Considered graphs
are obtained by connecting samples of the training set that belong to a same
class depending on the similarity of their representation in a latent space. As
a matter of fact, by looking at the features in latent representations of
samples as graph signals, it is possible to filter them in order to remove high
frequencies, thus improving the signal-to-noise ratio. A consequence is that
intra-class variance gets smaller, while mean remains the same, as shown
theoretically in this article. We support this analysis through experimental
evaluation of the graph filtering impact on the accuracy of multiple standard
benchmarks of the field. While our approach applies to all classification
problems in general, it is particularly useful in few-shot settings, where
intra-class noise has a huge impact due to initial sample selection.



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