Scalable Second-order Riemannian Optimization for $K$-means Clustering

• URL: http://arxiv.org/abs/2509.21675v1
• Date: Thu, 25 Sep 2025 22:49:00 GMT
• Title: Scalable Second-order Riemannian Optimization for $K$-means Clustering
• Authors: Peng Xu, Chun-Ying Hou, Xiaohui Chen, Richard Y. Zhang,
• Abstract summary: We present a new formulation of the $K$-means clustering problem as a submanifold.<n>By factorizing the $K$-means manifold into a product manifold, we show how each Newton subproblem can be solved.<n>Our experiments show that the proposed method converges significantly faster than the state-of-the-art first-order negative non-negative low-rank factorization method.
• Score: 22.839486642997187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering is a hard discrete optimization problem. Nonconvex approaches such as low-rank semidefinite programming (SDP) have recently demonstrated promising statistical and local algorithmic guarantees for cluster recovery. Due to the combinatorial structure of the $K$-means clustering problem, current relaxation algorithms struggle to balance their constraint feasibility and objective optimality, presenting tremendous challenges in computing the second-order critical points with rigorous guarantees. In this paper, we provide a new formulation of the $K$-means problem as a smooth unconstrained optimization over a submanifold and characterize its Riemannian structures to allow it to be solved using a second-order cubic-regularized Riemannian Newton algorithm. By factorizing the $K$-means manifold into a product manifold, we show how each Newton subproblem can be solved in linear time. Our numerical experiments show that the proposed method converges significantly faster than the state-of-the-art first-order nonnegative low-rank factorization method, while achieving similarly optimal statistical accuracy.

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