Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression?. (arXiv:2304.09868v1 [cs.LG])

Support vector clustering is an important clustering method. However, it
suffers from a scalability issue due to its computational expensive cluster
assignment step. In this paper we accelertate the support vector clustering via
spectrum-preserving data compression. Specifically, we first compress the
original data set into a small amount of spectrally representative aggregated
data points. Then, we perform standard support vector clustering on the
compressed data set. Finally, we map the clustering results of the compressed
data set back to discover the clusters in the original data set. Our extensive
experimental results on real-world data set demonstrate dramatically speedups
over standard support vector clustering without sacrificing clustering quality.



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