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Hyperdimensional Computing (HDC) is a computation framework based onproperties of high-dimensional random spaces. It is particularly useful formachine learning in resource-constrained environments, such as embedded systemsand IoT, as it achieves a good balance between accuracy, efficiency androbustness. The mapping of information to the hyperspace, named encoding, isthe most important stage in HDC. At its heart are basis-hypervectors,responsible for representing the smallest units of meaningful information. Inthis work we present a detailed study on basis-hypervector sets, which leads topractical contributions to HDC in general: 1) we propose an improvement forlevel-hypervectors, used to encode real numbers; 2) we introduce a method tolearn from circular data, an important type of information never beforeaddressed in machine learning with HDC. Empirical results indicate that thesecontributions lead to considerably more accurate models for both classificationand regression with circular data.

An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing