# Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification

arXiv: https://arxiv.org/abs/2108.10427 · HTML: https://mlsanity.com/paper/2108.10427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [Charting the Right Manifold: Manifold Mixup for Few-shot Learning](https://mlsanity.com/paper/1907.12087/) (2019)
- [Network Topology Inference from Spectral Templates](https://mlsanity.com/paper/1608.03008/) (2016)
- [Decoupling of brain function from structure reveals regional behavioral specialization in humans](https://mlsanity.com/paper/1905.07813/) (2019)
- [Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction](https://mlsanity.com/paper/1703.01842/) (2017)
- [Extracting representations of cognition across neuroimaging studies improves brain decoding](https://mlsanity.com/paper/1809.06035/) (2018)
- [Hierarchical Protein Function Prediction with Tail-GNNs](https://mlsanity.com/paper/2007.12804/) (2020)
- [On the choice of graph neural network architectures](https://mlsanity.com/paper/1911.05384/) (2019)
- [Gradients of Connectivity as Graph Fourier Bases of Brain Activity](https://mlsanity.com/paper/2009.12567/) (2020)
- [Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging](https://mlsanity.com/paper/1910.05149/) (2019)

## Abstract (arXiv)

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.