01423nas a2200157 4500008004100000245006500041210006300106260001400169520093400183100002401117700002601141700002101167700002101188700001901209856003701228 2020 eng d00aRay-based classification framework for high-dimensional data0 aRaybased classification framework for highdimensional data c10/1/20203 a
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.
1 aZwolak, Justyna, P.1 aKalantre, Sandesh, S.1 aMcJunkin, Thomas1 aWeber, Brian, J.1 aTaylor, J., M. uhttps://arxiv.org/abs/2010.00500