Approximating Uniform Random Rotations by Two-Stage Structured Hadamard Rotations in High Dimensions

Uniform random rotations are useful in fast Johnson--Lindenstrauss embeddings, kernel approximation, communication-efficient learning, and recent AI compression pipelines, but are computationally expensive to generate and apply in high dimensions. A practical alternative repeats Walsh--Hadamard tran