Experimental Results regarding multiple Machine Learning via Quaternions
Tianlei Zhu Renzhe Zhu
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.