New Fourier Approach to Gaussian Mixture Learning Detailed

By PulseAugur Editorial · [1 sources] · 2026-07-13 04:00

This paper introduces a novel analytical approach using Fourier analysis to learn Gaussian mixture models. The proposed randomized algorithm can identify the centers of Gaussian components within a mixture, even with a large number of components and in high dimensions. The research also addresses scenarios with unknown weights and provides theoretical bounds on sample and computational complexity, suggesting its tightness compared to existing methods. AI

RANK_REASON The cluster contains an academic paper detailing a new algorithm and theoretical analysis for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

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Somnath Chakraborty

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New Fourier Approach to Gaussian Mixture Learning Detailed

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arXiv cs.LG TIER_1 English(EN) · Somnath Chakraborty, Hariharan Narayanan · 2026-07-13 04:00

A Fourier analytique approach to Gaussian mixture learning

arXiv:2004.05813v3 Announce Type: replace-cross Abstract: Suppose that we are given independent, identically distributed random samples $x_1,\cdots,x_n$ from a mixture at most $k$ many $d$-dimensional spherical Gaussian distributions $\mu_1,\cdots,\mu_{k_0}$ of identical and know…

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A Fourier analytique approach to Gaussian mixture learning

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