Ray-based classification framework for high-dimensional data

Published

February 3, 2020

Author(s)

Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber

Abstract

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 Title

Proceedings of the Third Workshop on Machine Learning and the Physical Sciences

Conference Dates

December 5-12, 2020

Conference Location

Vancouver, CA

Conference Title

Workshop on Machine Learning and the Physical Sciences

Pub Type

Conferences

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Keywords

machine learning, quantum dots, high-dimensional data, classification

Citation

Zwolak, J. , Taylor, J. , Kalantre, S. , McJunkin, T. and Weber, B. (2020), 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 October 10, 2025)