$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets.    Semidefinite programming (SDP) relaxations have recently been proposed for solving  the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets.    By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used by machine learning practitioners, but without a solid statistical underpinning nor rigorous guarantees. In this paper, we describe an NMF-like algorithm that works by solving a \emph{nonnegative} low-rank restriction of the SDP relaxed $K$-means formulation using a nonconvex Burer--Monteiro factorization approach. The resulting algorithm is just as simple and scalable as state-of-the-art NMF algorithms, while also enjoying the same strong statistical optimality guarantees as the SDP.     In our experiments, we observe that our algorithm achieves substantially smaller mis-clustering errors compared to the existing state-of-the-art.
Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming
Yubo Zhuang Xiaohui Chen Yun Yang Richard Y. Zhang