Cluster Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification

• URL: http://arxiv.org/abs/2107.01345v1
• Date: Sat, 3 Jul 2021 04:51:07 GMT
• Title: Cluster Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification
• Authors: Jaroslav Hlav\'a\v{c}, Martin Kopp, Jan Kohout
• Abstract summary: In this paper, we present CRS, a novel method for selecting a small yet representative subset of objects as a cluster prototype. Memory and computationally efficient selection of representatives is enabled by leveraging the similarity graph representation of each cluster created by the NN-Descent algorithm. CRS can be used in an arbitrary metric or non-metric space because of the graph-based approach, which requires only a pairwise similarity measure.
• Score: 4.176752121302988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The nearest prototype classification is a less computationally intensive replacement for the $k$-NN method, especially when large datasets are considered. In metric spaces, centroids are often used as prototypes to represent whole clusters. The selection of cluster prototypes in non-metric spaces is more challenging as the idea of computing centroids is not directly applicable. In this paper, we present CRS, a novel method for selecting a small yet representative subset of objects as a cluster prototype. Memory and computationally efficient selection of representatives is enabled by leveraging the similarity graph representation of each cluster created by the NN-Descent algorithm. CRS can be used in an arbitrary metric or non-metric space because of the graph-based approach, which requires only a pairwise similarity measure. As we demonstrate in the experimental evaluation, our method outperforms the state of the art techniques on multiple datasets from different domains.

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