# Confident Clustering via PCA Compression Ratio and Its Application to Single-cell RNA-seq Analysis

arXiv: https://arxiv.org/abs/2205.09849 · HTML: https://mlsanity.com/paper/2205.09849/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 4 of 4)

- [Clustering with Noisy Queries](https://mlsanity.com/paper/1706.07510/) (2017)
- [Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle](https://mlsanity.com/paper/2106.10374/) (2021)
- [Recovering Unbalanced Communities in the Stochastic Block Model With Application to Clustering with a Faulty Oracle](https://mlsanity.com/paper/2202.08522/) (2022)
- [Capturing the Denoising Effect of PCA via Compression Ratio](https://mlsanity.com/paper/2204.10888/) (2022)

## Abstract (arXiv)

Unsupervised clustering algorithms for vectors has been widely used in the area of machine learning. Many applications, including the biological data we studied in this paper, contain some boundary datapoints which show combination properties of two underlying clusters and could lower the performance of the traditional clustering algorithms. We develop a confident clustering method aiming to diminish the influence of these datapoints and improve the clustering results. Concretely, for a list of datapoints, we give two clustering results. The first-round clustering attempts to classify only pure vectors with high confidence. Based on it, we classify more vectors with less confidence in the second round. We validate our algorithm on single-cell RNA-seq data, which is a powerful and widely used tool in biology area. Our confident clustering shows a high accuracy on our tested datasets. In addition, unlike traditional clustering methods in single-cell analysis, the confident clustering shows high stability under different choices of parameters.