Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression. (arXiv:2304.09868v2 [cs.LG] UPDATED)

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 …

aggregated data arxiv cluster clustering compression computational data data compression data set issue map paper scalability set small spectrum standard support vector