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GEM-KMeans: Memory-Efficient and Accurate Clustering on Massive Scale with GPU Optimization
Memory-efficient scaling on clustering problems without sacrificing statistical accuracy is of central interest for large-scale data analysis and machine learning problems. Nonnegative low-rank (NLR) matrix factorization for $K$-means is a scalable clustering method, which connects to semidefinite r
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