Ray-based classification framework for high-dimensional data

TitleRay-based classification framework for high-dimensional data
Publication TypeJournal Article
Year of Publication2020
AuthorsZwolak, JP, Kalantre, SS, McJunkin, T, Weber, BJ, Taylor, JM
JournalProceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada
Date Published10/1/2020

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 \emph{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.