# Blessing of dimensionality at the edge

arXiv: https://arxiv.org/abs/1910.00445 · HTML: https://mlsanity.com/paper/1910.00445/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [High-dimensional separability for one- and few-shot learning](https://mlsanity.com/paper/2106.15416/) (2021)
- [Learning from scarce information: using synthetic data to classify Roman fine ware pottery](https://mlsanity.com/paper/2107.01401/) (2021)
- [Agile gesture recognition for capacitive sensing devices: adapting on-the-job](https://mlsanity.com/paper/2305.07624/) (2023)
- [Agile gesture recognition for low-power applications: customisation for generalisation](https://mlsanity.com/paper/2403.15421/) (2024)

## Abstract (arXiv)

In this paper we present theory and algorithms enabling classes of Artificial Intelligence (AI) systems to continuously and incrementally improve with a-priori quantifiable guarantees - or more specifically remove classification errors - over time. This is distinct from state-of-the-art machine learning, AI, and software approaches. Another feature of this approach is that, in the supervised setting, the computational complexity of training is linear in the number of training samples. At the time of classification, the computational complexity is bounded by few inner product calculations. Moreover, the implementation is shown to be very scalable. This makes it viable for deployment in applications where computational power and memory are limited, such as embedded environments. It enables the possibility for fast on-line optimisation using improved training samples. The approach is based on the concentration of measure effects and stochastic separation theorems and is illustrated with an example on the identification faulty processes in Computer Numerical Control (CNC) milling and with a case study on adaptive removal of false positives in an industrial video surveillance and analytics system.