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This paper presents an experimental study on the application of quaternionsin several machine learning algorithms. Quaternion is a mathematicalrepresentation of rotation in three-dimensional space, which can be used torepresent complex data transformations. In this study, we explore the use ofquaternions to represent and classify rotation data, using randomly generatedquaternion data and corresponding labels, converting quaternions to rotationmatrices, and using them as input features. Based on quaternions and multiplemachine learning algorithms, it has shown higher accuracy and significantlyimproved performance in prediction tasks. Overall, this study provides anempirical basis for exploiting quaternions for machine learning tasks.

Experimental Results regarding multiple Machine Learning via Quaternions