Abstract
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