Efficient quadratic entropy with distance sketches
The method uses random feature embeddings and projections to approximate quadratic entropy for common distances like Euclidean and spherical geodesic cases. This approach significantly reduces computational complexity, allowing for large-scale computation through amortization techniques. It provides unbiased estimators with error bounds independent of the ambient dimension.