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