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arXiv · 2010.00500

Ray-based classification framework for high-dimensional data

Justyna P. Zwolak·Sandesh S. Kalantre·Thomas McJunkin·Brian J. Weber·Jacob M. Taylor
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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 \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.

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Justyna P. Zwolak, Sandesh S. Kalantre, Thomas McJunkin, Brian J. Weber, Jacob M. Taylor. 2020-10-01. Ray-based classification framework for high-dimensional data. https://arxiv.org/abs/2010.00500

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