Authors:
(1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA ([email protected]);
(2) Anthony Thomas, University of California San Diego, La Jolla, California, USA ([email protected]);
(3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico ([email protected]);
(4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA ([email protected]);
(5) Tajana ล imuniฤ Rosing, University of California San Diego, La Jolla, USA ([email protected]).
Table of Links
8 Evaluation of LifeHD semi and LifeHDa
9 Discussions and Future Works
10 Conclusion, Acknowledgments, and References
3 BACKGROUND ON HDC
Hyperdimensional Computing (HDC) is an emerging paradigm for information processing from the cognitive-neuroscience literature [24]. In HDC, all computation is performed on low-precision and distributed representations of data that accord naturally with highly parallel and low-energy hardware.
The encoding function ๐ : X โ H embeds data from its ambient representation into HD-space. In general, encoding should preserve some meaningful notion of similarity between input points in the sense that ๐ (๐ฅ) ยท ๐ (๐ฅ โฒ ) โ ๐ (๐ฅ, ๐ฅโฒ ), where ๐ is some similarity function of interest on X. In this paper, we use spatiotemporal encoding for time series sensor data, and HDnn for more complex data, such as images, which we explain in the following.
This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.