Improving Classification Accuracy with Graph Filtering
- URL: http://arxiv.org/abs/2101.04789v2
- Date: Mon, 25 Jan 2021 18:24:06 GMT
- Title: Improving Classification Accuracy with Graph Filtering
- Authors: Mounia Hamidouche, Carlos Lassance, Yuqing Hu, Lucas Drumetz, Bastien
Pasdeloup, Vincent Gripon
- Abstract summary: We show that the proposed graph filtering methodology has the effect of reducing intra-class variance, while maintaining the mean.
While our approach applies to all classification problems in general, it is particularly useful in few-shot settings, where intra-class noise can have a huge impact due to the small sample selection.
- Score: 9.153817737157366
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. We
show that the proposed graph filtering methodology has the effect of
asymptotically reducing intra-class variance, while maintaining the mean. While
our approach applies to all classification problems in general, it is
particularly useful in few-shot settings, where intra-class noise can have a
huge impact due to the small sample selection. Using standardized benchmarks in
the field of vision, we empirically demonstrate the ability of the proposed
method to slightly improve state-of-the-art results in both cases of few-shot
and standard classification.
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