OneBatchPAM: A Fast and Frugal K-Medoids Algorithm

• URL: http://arxiv.org/abs/2501.19285v1
• Date: Fri, 31 Jan 2025 16:48:16 GMT
• Title: OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
• Authors: Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger, Mathilde Mougeot, Nicolas Vayatis,
• Abstract summary: This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. A single batch of size m n provides the estimation, which reduces the required memory size and the number of pairwise dissimilarities computations to O(mn), instead of O(n2) compared to most k-medoids baselines. We obtain theoretical results highlighting that a batch of size m = O(log(n)) is sufficient to guarantee, with strong probability, the same performance as the original local-search algorithm.
• Score: 6.69456225406097
• License:
• Abstract: This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search algorithm that iteratively improves the medoid selection based on the estimation of the k-medoids objective. A single batch of size m << n provides the estimation, which reduces the required memory size and the number of pairwise dissimilarities computations to O(mn), instead of O(n^2) compared to most k-medoids baselines. We obtain theoretical results highlighting that a batch of size m = O(log(n)) is sufficient to guarantee, with strong probability, the same performance as the original local-search algorithm. Multiple experiments conducted on real datasets of various sizes and dimensions show that our algorithm provides similar performances as state-of-the-art methods such as FasterPAM and BanditPAM++ with a drastically reduced running time.

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