Phase transitions and optimal algorithms in high-dimensional Gaussian   mixture clustering
  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. 
Thibault Lesieur, Caterina De Bacco, Jess Banks, Florent Krzakala,   Cris Moore and Lenka Zdeborov\'a
stat.ML cond-mat.dis-nn cs.IT math.IT