Hypercomplex Algebra

Hypercomplex algebra extends the concept of numbers beyond real and complex numbers, offering richer mathematical structures for representing and processing multidimensional data. Current research focuses on applying these algebras to improve neural network architectures, particularly in areas like time series forecasting, image classification, and natural language processing, with models employing hypercomplex layers and novel algorithms like those based on the Least Mean Square method. This work aims to leverage the unique properties of hypercomplex numbers to enhance the performance, efficiency, and robustness of machine learning models, impacting fields ranging from AI to knowledge graph completion.