# Iterative Spectral Method for Alternative Clustering

arXiv: https://arxiv.org/abs/1909.03441 · HTML: https://mlsanity.com/paper/1909.03441/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

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

- [Deep Layer-wise Networks Have Closed-Form Weights](https://mlsanity.com/paper/2202.01210/) (2022)
- [Sanitized Clustering against Confounding Bias](https://mlsanity.com/paper/2311.01252/) (2023)

## Abstract (arXiv)

Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.