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Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering

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Abstract

We consider the problem of Gaussian mixture clustering in the high-dimensional limit where the data consists of $m$ points in $n$ dimensions, $n,m \rightarrow \infty$ and $\alpha = m/n$ stays finite. Using exact but non-rigorous methods from statistical physics, we determine the critical value of $\alpha$ and the distance between the clusters at which it becomes information-theoretically possible to reconstruct the membership into clusters better than chance. We also determine the accuracy achievable by the Bayes-optimal estimation algorithm. In particular, we find that when the number of clusters is sufficiently large, $r > 4 + 2 \sqrt{\alpha}$, there is a gap between the threshold for information-theoretically optimal performance and the threshold at which known algorithms succeed.
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cea-01448112 , version 1 (27-01-2017)

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Thibault Lesieur, Caterina de Bacco, Jess Banks, Florent Krzakala, Cris Moore, et al.. Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering. 2016. ⟨cea-01448112⟩
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