Mixed-Curvature Decision Trees and Random Forests
This provides a more expressive method for machine learning tasks in complex geometric spaces, addressing a gap where only linear classifiers existed and no regressors were available.
The authors tackled the problem of classification and regression in product space manifolds by extending decision tree and random forest algorithms to these spaces, resulting in superior accuracy compared to Euclidean methods across various constant-curvature and product manifolds.
We extend decision tree and random forest algorithms to product space manifolds: Cartesian products of Euclidean, hyperspherical, and hyperbolic manifolds. Such spaces have extremely expressive geometries capable of representing many arrangements of distances with low metric distortion. To date, all classifiers for product spaces fit a single linear decision boundary, and no regressor has been described. Our method enables a simple, expressive method for classification and regression in product manifolds. We demonstrate the superior accuracy of our tool compared to Euclidean methods operating in the ambient space or the tangent plane of the manifold across a range of constant-curvature and product manifolds. Code for our implementation and experiments is available at https://github.com/pchlenski/embedders.