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

TL;DR

This paper introduces a noise-insensitive clustering method for multi-dimensional signals, enabling shape-based grouping that is robust to amplitude variations and scalable for complex imaging modalities.

Contribution

A novel shape-based clustering algorithm using angular distance and direction-based centroids, improving robustness and scalability in multi-dimensional feature learning.

Findings

Effective clustering of signals based on shape, not amplitude

Robustness to noise and amplitude variations demonstrated

Scalable method suitable for complex imaging data

Abstract

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.