Non-asymptotic quantisation of spherically symmetric distributions
Researchers have developed a new method for non-asymptotic quantization of spherically symmetric distributions, addressing limitations of Zador's theorem in high dimensions. The proposed approach utilizes random quantizers uniformly distributed on a sphere, achieving exceptional performance with moderate sample sizes. This method allows for precise computation of expected distortion and efficient numerical determination of the optimal radius, with approximations derived from extreme-value theory for scenarios where sample size scales with dimension. AI
IMPACT Introduces a novel statistical technique that could improve data representation and efficiency in high-dimensional AI models.