Graph Filtering for Improving the Accuracy of Classification problems

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.