Bringing the Blessing of Dimensionality to the Edge
Ivan Tyukin, Alexander N. Gorban, Alistair A. McEwan, Sepehr Meshkinfamfard · 2019
In this work we present a novel approach and algorithms for equipping Artificial Intelligence systems with capabilities to become better over time. A distinctive feature of the approach is that, in the supervised setting, the approaches' computational complexity is sub-linear in the number of training samples. This makes it particularly attractive in applications in which the computational power and memory are limited. The approach is based on the concentration of measure effects and stochastic separation theorems. The algorithms are illustrated with examples.