Ray-based classification framework for high-dimensional data
Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than using dense, multi-dimensional data, we propose a deep neural network (DNN) classification framework that utilizes a minimal collection of one-dimensional representations, called rays, to construct the "fingerprint" of the structure(s) based on substantially reduced information. We empirically study this framework using a synthetic dataset of double and triple quantum dot devices and apply it to the classification problem of identifying the device state. We show that the performance of the ray-based classifier is already on par with traditional 2D images for low dimensional systems, while significantly cutting down the data acquisition cost.
Proceedings of the Third Workshop on Machine Learning and the Physical Sciences
December 5-12, 2020
Workshop on Machine Learning and the Physical Sciences
, Taylor, J.
, Kalantre, S.
, McJunkin, T.
and Weber, B.
Ray-based classification framework for high-dimensional data, Proceedings of the Third Workshop on Machine Learning and the Physical Sciences, Vancouver, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930482
(Accessed December 7, 2023)