# Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression

arXiv: https://arxiv.org/abs/2304.09868 · HTML: https://mlsanity.com/paper/2304.09868/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 1 of 1)

- [Improving Spectral Clustering Using Spectrum-Preserving Node Aggregation](https://mlsanity.com/paper/2110.12328/) (2021)

## Built on by (most cited first, 2 of 2)

- [Can LLMs Lie? Investigation beyond Hallucination](https://mlsanity.com/paper/2509.03518/) (2025)
- [Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point](https://mlsanity.com/paper/2306.04425/) (2023)

## Abstract (arXiv)

This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key cluster properties of the original data sets based on a novel spectral data compression approach. Then, the resultant spectrally-compressed data sets are leveraged for the development of fast and high quality algorithm for support vector clustering. We conducted extensive experiments using real-world data sets and obtained very promising results. The proposed method allows us to achieve 100X and 115X speedups over the state of the art SVC method on the Pendigits and USPS data sets, respectively, while achieving even better clustering quality. To the best of our knowledge, this represents the first practical method for high-quality and fast SVC on large-scale real-world data sets