Experimental Results regarding multiple Machine Learning via Quaternions

TL;DR

This paper empirically investigates the use of quaternions in various machine learning algorithms, demonstrating improved accuracy and performance in rotation data classification tasks.

Contribution

It introduces the application of quaternions as input features in machine learning, showing their effectiveness in enhancing prediction accuracy.

Findings

Higher accuracy achieved with quaternion-based features

Significant performance improvements in prediction tasks

Empirical evidence supporting quaternion use in ML

Abstract

This paper presents an experimental study on the application of quaternions in several machine learning algorithms. Quaternion is a mathematical representation of rotation in three-dimensional space, which can be used to represent complex data transformations. In this study, we explore the use of quaternions to represent and classify rotation data, using randomly generated quaternion data and corresponding labels, converting quaternions to rotation matrices, and using them as input features. Based on quaternions and multiple machine learning algorithms, it has shown higher accuracy and significantly improved performance in prediction tasks. Overall, this study provides an empirical basis for exploiting quaternions for machine learning tasks.