Computationally efficient sparse clustering

Abstract

Abstract We study statistical and computational limits of clustering when the means of the centres are sparse and their dimension is possibly much larger than the sample size. Our theoretical analysis focuses on the model $X_i=z_i \theta +\varepsilon _{i}, \ z_i \in \{-1,1\}, \ \varepsilon _i \thicksim \mathcal{N}(0, I)$, which has two clusters with centres $\theta $ and $-\theta $. We provide a finite sample analysis of a new sparse clustering algorithm based on sparse Principal Component Analysis (PCA) and show that it achieves the minimax optimal misclustering rate in the regime $\|\theta \| \rightarrow \infty $. Our results require the sparsity to grow slower than the square root of the sample size. Using a recent framework for computational lower bounds—the low-degree likelihood ratio—we give evidence that this condition is necessary for any polynomial-time clustering algorithm to succeed below the Baik-Ben Arous-Péché (BBP) threshold. This complements the existing evidence based on reductions and statistical query lower bounds. Compared with these existing results, we cover a wider set of parameter regimes and give a more precise understanding of the runtime required and the misclustering error achievable. Our results imply that a large class of tests based on low-degree polynomials fail to solve even the weak testing task.