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Found 4 papers, 2 papers with code
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
In contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm widely used by machine learning practitioners, but it lacks a solid statistical underpinning and theoretical guarantees.
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Clustering is a widely deployed unsupervised learning tool.
Wasserstein $K$-means for clustering probability distributions
Clustering is an important exploratory data analysis technique to group objects based on their similarity.
Sketch-and-Lift: Scalable Subsampled Semidefinite Program for $K$-means Clustering
Semidefinite programming (SDP) is a powerful tool for tackling a wide range of computationally hard problems such as clustering.
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