@inproceedings{
zhao2023unleashing,
title={Unleashing Hyperdimensional Computing with Nystr\"om Method based Encoding},
author={Quanling Zhao and Anthony Hitchcock Thomas and Xiaofan Yu and Tajana Rosing},
booktitle={Machine Learning with New Compute Paradigms},
year={2023},
url={https://openreview.net/forum?id=7YUt7pQVFg}
}
Hyperdimensional computing (HDC) is an approach for solving cognitive information processing tasks using data represented as high dimensional, low-precision, vectors. The technique has a rigorous mathematical backing, and is easy to implement in energy-efficient and highly parallelizable hardware like FPGAs and ``in-memory'' architectures. The success of HDC based machine learning approaches is heavily dependent on the mapping from raw data to high-dimensional space. In this work, we propose a new method for constructing this mapping that is based on the Nyström  method from the literature on kernel approximation. Our approach provides a simple recipe to turn any user-defined positive-semidefinite similarity function into an equivalent mapping in HDC. There is a vast literature on the design of such functions for learning problems, and our approach provides a mechanism to import them into the HDC setting, potentially expanding the types of problems that can be tackled using HDC. An empirical comparison of our approach against existing HDC encoding methods on a variety of classification tasks shows that we can achieve 10%-37% and 3%-18% better classification accuracy on graph and string datasets respectively.
Unleashing Hyperdimensional Computing with Nyström Method based Encoding
Quanling Zhao Anthony Hitchcock Thomas Xiaofan Yu Tajana Rosing