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

10/10/2016
by   Thibault Lesieur, et al.
0

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 →∞ and α = m/n stays finite. Using exact but non-rigorous methods from statistical physics, we determine the critical value of α 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 √(α), there is a gap between the threshold for information-theoretically optimal performance and the threshold at which known algorithms succeed.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset