High-Dimensional Data Structures in Adaptive Neural Networks

Abstract

This paper introduces a novel adaptive graph neural network (GNN) architecture designed specifically for high-dimensional data. Traditional GNNs often struggle with the complexity of high-dimensional data, leading to slow convergence and suboptimal performance. This work proposes a dynamically adjustable graph structure through a combination of dimensionality reduction techniques and adaptive parameter updates. The core mechanism focuses on leveraging the inherent self-adaptation properties of high-dimensional data structures to optimize the network's representation and feature extraction. We demonstrate the effectiveness of our proposed architecture through extensive experimentation, showcasing improved accuracy and training speed compared to existing state-of-the-art GNNs on benchmark datasets. The study highlights the potential of dynamic graph structure adjustment to overcome the limitations of conventional GNNs in high-dimensional scenarios.