Ray-based classification framework for high-dimensional data
This work addresses data acquisition costs in classification for quantum dot devices, but it is incremental as it builds on existing methods with a novel data representation.
The paper tackles classification of high-dimensional data by proposing a ray-based deep neural network framework that uses minimal one-dimensional representations to construct fingerprints, showing performance on par with traditional 2D images for low-dimensional systems while reducing data acquisition costs.
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.