Generative Expansion of Small Datasets: An Expansive Graph Approach
This addresses data scarcity in machine learning applications, though it appears incremental as it builds on existing methods like autoencoders and optimal transport.
The paper tackles the problem of limited data availability in machine learning by introducing an Expansive Synthesis model that generates large-scale, information-rich datasets from minimal samples, achieving comparable classifier performance to original datasets.
Limited data availability in machine learning significantly impacts performance and generalization. Traditional augmentation methods enhance moderately sufficient datasets. GANs struggle with convergence when generating diverse samples. Diffusion models, while effective, have high computational costs. We introduce an Expansive Synthesis model generating large-scale, information-rich datasets from minimal samples. It uses expander graph mappings and feature interpolation to preserve data distribution and feature relationships. The model leverages neural networks' non-linear latent space, captured by a Koopman operator, to create a linear feature space for dataset expansion. An autoencoder with self-attention layers and optimal transport refines distributional consistency. We validate by comparing classifiers trained on generated data to those trained on original datasets. Results show comparable performance, demonstrating the model's potential to augment training data effectively. This work advances data generation, addressing scarcity in machine learning applications.