Experimental Results regarding multiple Machine Learning via Quaternions
This work addresses the challenge of representing and classifying rotation data for applications in fields like robotics or computer vision, but it appears incremental as it builds on existing quaternion representations without introducing a new method.
The paper tackled the problem of classifying rotation data by applying quaternions as input features in multiple machine learning algorithms, resulting in higher accuracy and significantly improved performance in prediction tasks.
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.