Ray-based classification framework for high-dimensional data

TL;DR

This paper introduces a ray-based deep neural network framework that classifies high-dimensional data efficiently using minimal one-dimensional representations, reducing data acquisition costs while maintaining accuracy.

Contribution

The paper proposes a novel ray-based DNN classification method that uses low-dimensional fingerprints, offering a cost-effective alternative to dense data in high-dimensional classification tasks.

Findings

Performance comparable to traditional methods on low-dimensional systems

Significantly reduces data acquisition costs

Effective in classifying quantum dot device states

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.