We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide theoretical results on an optimal regime for the GSE, namely a feasibility region for its parameters, and demonstrate the experimental relevance of our findings. Next, we introduce a strategy to gain insight on which interactions are responsible for the certain predictions, paving the way for a far more transparent model. In an empirical evaluation on a real-world clinical cohort containing patients with suspected coronary artery disease, the GSE achieves far better performance than traditional algorithms.
@article{arxiv.1907.13443,
title = {Graph Space Embedding},
author = {João Pereira and Albert Groen and Erik Stroes and Evgeni Levin},
journal= {arXiv preprint arXiv:1907.13443},
year = {2019}
}