Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data
This work addresses the problem of enhancing semi-supervised learning for biomedical tabular data, representing an incremental improvement with domain-specific applications.
The paper tackles the problem of insufficient exploitation of inter-instance relationships in semi-supervised learning by proposing an approach to infer latent graphs that capture intrinsic data relationships, and it demonstrates that this methodology outperforms state-of-the-art methods on three biomedical datasets.
In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data. In this work, we address this limitation by providing an approach for inferring latent graphs that capture the intrinsic data relationships. By leveraging graph-based representations, our approach facilitates the seamless propagation of information throughout the graph, effectively incorporating global and local knowledge. Through evaluations on biomedical tabular datasets, we compare the capabilities of our approach to other contemporary methods. Our work demonstrates the significance of inter-instance relationship discovery as practical means for constructing robust latent graphs to enhance semi-supervised learning techniques. The experiments show that the proposed methodology outperforms contemporary state-of-the-art methods for (semi-)supervised learning on three biomedical datasets.