The graph embedding approach utilized in the GE-GB-RVFL model, while effective in preserving the geometric structure of the dataset, has several potential limitations. One significant limitation is the computational complexity associated with constructing and maintaining the graph, particularly as the size of the dataset increases. The adjacency matrix and the associated graph structures can become large and unwieldy, leading to increased memory usage and slower processing times.
To address this limitation, one could explore sparse graph techniques that reduce the number of edges in the graph while still capturing essential relationships among data points. Techniques such as k-nearest neighbors (KNN) can be employed to limit the connections to only the most relevant nodes, thereby simplifying the graph structure and reducing computational overhead.
Another limitation is the potential for overfitting, especially in high-dimensional spaces where the graph may capture noise rather than meaningful relationships. Regularization techniques can be integrated into the graph embedding process to mitigate this risk. Additionally, employing dimensionality reduction methods prior to graph construction can help in focusing on the most informative features, thus enhancing the robustness of the graph embedding.