# K-Means for Noise-Insensitive Multi-Dimensional Feature Learning

arXiv: https://arxiv.org/abs/2202.07754 · HTML: https://mlsanity.com/paper/2202.07754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 15 Feb 2022.

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

- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms](https://mlsanity.com/paper/2008.05171/) (2020)
- [Improving Maximal Safe Brain Tumor Resection with Photoacoustic Remote Sensing Microscopy](https://mlsanity.com/paper/2009.10226/) (2020)
- [Histopathology for Mohs Micrographic Surgery with Photoacoustic Remote Sensing Microscopy](https://mlsanity.com/paper/2009.08337/) (2020)

## Abstract (arXiv)

Many measurement modalities which perform imaging by probing an object pixel-by-pixel, such as via Photoacoustic Microscopy, produce a multi-dimensional feature (typically a time-domain signal) at each pixel. In principle, the many degrees of freedom in the time-domain signal would admit the possibility of significant multi-modal information being implicitly present, much more than a single scalar "brightness", regarding the underlying targets being observed. However, the measured signal is neither a weighted-sum of basis functions (such as principal components) nor one of a set of prototypes (K-means), which has motivated the novel clustering method proposed here. Signals are clustered based on their shape, but not amplitude, via angular distance and centroids are calculated as the direction of maximal intra-cluster variance, resulting in a clustering algorithm capable of learning centroids (signal shapes) that are related to the underlying, albeit unknown, target characteristics in a scalable and noise-robust manner.