Cutoff for exact recovery of Gaussian mixture models
Statistics Theory
2020-12-01 v3 Data Structures and Algorithms
Information Theory
math.IT
Probability
Machine Learning
Statistics Theory
Abstract
We determine the information-theoretic cutoff value on separation of cluster centers for exact recovery of cluster labels in a -component Gaussian mixture model with equal cluster sizes. Moreover, we show that a semidefinite programming (SDP) relaxation of the -means clustering method achieves such sharp threshold for exact recovery without assuming the symmetry of cluster centers.
Keywords
Cite
@article{arxiv.2001.01194,
title = {Cutoff for exact recovery of Gaussian mixture models},
author = {Xiaohui Chen and Yun Yang},
journal= {arXiv preprint arXiv:2001.01194},
year = {2020}
}