



@article {2685,
	title = {Ray-based classification framework for high-dimensional data},
	journal = {Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada},
	year = {2020},
	month = {10/1/2020},
	abstract = {<p>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 \&quot;fingerprint\&quot; 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.</p>},
	url = {https://arxiv.org/abs/2010.00500},
	author = {Justyna P. Zwolak and Sandesh S. Kalantre and Thomas McJunkin and Brian J. Weber and J. M. Taylor}
}
