# Fast Kernel k-means Clustering Using Incomplete Cholesky Factorization

arXiv: https://arxiv.org/abs/2002.02846 · HTML: https://mlsanity.com/paper/2002.02846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [On The Relative Error of Random Fourier Features for Preserving Kernel Distance](https://mlsanity.com/paper/2210.00244/) (2022)

## Abstract (arXiv)

Kernel-based clustering algorithm can identify and capture the non-linear structure in datasets, and thereby it can achieve better performance than linear clustering. However, computing and storing the entire kernel matrix occupy so large memory that it is difficult for kernel-based clustering to deal with large-scale datasets. In this paper, we employ incomplete Cholesky factorization to accelerate kernel clustering and save memory space. The key idea of the proposed kernel k-means clustering using incomplete Cholesky factorization is that we approximate the entire kernel matrix by the product of a low-rank matrix and its transposition. Then linear k-means clustering is applied to columns of the transpose of the low-rank matrix. We show both analytically and empirically that the performance of the proposed algorithm is similar to that of the kernel k-means clustering algorithm, but our method can deal with large-scale datasets.