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A Fourier Approach to the Parameter Estimation Problem for One-dimensional Gaussian Mixture Models
April 22, 2024, 4:42 a.m. | Xinyu Liu, Hai Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: The purpose of this paper is twofold. First, we propose a novel algorithm for estimating parameters in one-dimensional Gaussian mixture models (GMMs). The algorithm takes advantage of the Hankel structure inherent in the Fourier data obtained from independent and identically distributed (i.i.d) samples of the mixture. For GMMs with a unified variance, a singular value ratio functional using the Fourier data is introduced and used to resolve the variance and component number simultaneously. The consistency …
abstract algorithm arxiv cs.lg data distributed eess.sp fourier independent novel paper parameters stat.me stat.ml the algorithm type
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