Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification
This work addresses the challenge of improving classification accuracy in scenarios with limited labeled data, which is incremental by building on existing graph-based priors.
The paper tackles the problem of few-shot classification by introducing a model that incorporates graph structure priors to handle underdetermined settings and reduce overfitting, resulting in increased generalization accuracy compared to popular alternatives on real datasets.
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 model where observed class signals are supposed to be deteriorated with two sources of noise, one independent of the underlying graph structure and isotropic, and the other colored by a known graph operator. Under this model, we derive an optimal methodology to classify such signals. Interestingly, this methodology includes a single parameter, making it particularly suitable for cases where available data is scarce. Using various real datasets, we showcase the ability of the proposed model to be implemented in real world scenarios, resulting in increased generalization accuracy compared to popular alternatives.