Low-degree polynomials lower bounds for clustering in small dimension

We study the computational limits of clustering $n$ observations drawn from a mixture of $K$ isotropic distributions in $\mathbb R^d$, in the regime where the number $K$ of groups is much larger than the dimension $d$. In this regime, tensor-based procedures recover the partition with a polylogarith