AI RESEARCH
Approximating Uniform Random Rotations by Two-Block Structured Hadamard Rotations in High Dimensions
arXiv CS.LG
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ArXi:2604.23418v1 Announce Type: new Uniform random rotations are a useful primitive in applications such as fast Johnson-Lindenstrauss embeddings, kernel approximation, communication-efficient learning, and recent AI compression pipelines, but they are computationally expensive to generate and apply in high dimensions. A common practical replacement is repeated structured random rotations built from Walsh-Hadamard transforms and random sign diagonals. Applying the structured random rotation twice has been shown empirically to be useful, but the ing theory is still limited.