Coreset of Hyperspectral Images on Small Quantum Computer

Autor:	Otgonbaatar, Soronzonbold, Datcu, Mihai, Demir, Begüm
Rok vydání:	2022
Předmět:	Quantum Physics Computer Science - Computer Vision and Pattern Recognition Computer Science - Emerging Technologies
Druh dokumentu:	Working Paper
Popis:	Machine Learning (ML) techniques are employed to analyze and process big Remote Sensing (RS) data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM is a quadratic programming (QP) problem, and a D-Wave quantum annealer (D-Wave QA) promises to solve this QP problem more efficiently than a conventional computer. However, the D-Wave QA cannot solve directly the SVM due to its very few input qubits. Hence, we use a coreset ("core of a dataset") of given EO data for training an SVM on this small D-Wave QA. The coreset is a small, representative weighted subset of an original dataset, and any training models generate competitive classes by using the coreset in contrast to by using its original dataset. We measured the closeness between an original dataset and its coreset by employing a Kullback-Leibler (KL) divergence measure. Moreover, we trained the SVM on the coreset data by using both a D-Wave QA and a conventional method. We conclude that the coreset characterizes the original dataset with very small KL divergence measure. In addition, we present our KL divergence results for demonstrating the closeness between our original data and its coreset. As practical RS data, we use Hyperspectral Image (HSI) of Indian Pine, USA. Comment: Accepted to IGARSS2022. You may not be able to access this article after the publication in the conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.04691 Zobrazit plný text záznamu View this record from Arxiv