(Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Piotr; Ślot, Krzysztof; Kucharski, Jacek
Hyperdimensional computing is a novel paradigm, capable of
processing complex data structures with simple operations. Its main limitations
lie in the conversion of real world data onto hyperdimensional space,
which due to lack of a universal translation scheme, oftentimes requires
application-specific methods. This work presents a novel method for unsupervised
hyperdimensional conversion of arbitrary image data. Additionally,
this method is augmented by the ability of creating HyperSymbols, or
class prototypes, provided that such class labels are available. The proposed
method achieves promising performance on MNIST dataset, both in translating
individual samples as well as producing HyperSymbols for downstream
classification task.
