L1-PCA with quantum annealing

Ian Tomeo; Panagiotis (Panos P.) Markopoulos; Andreas Savakis

doi:10.1117/12.3015944

10 June 2024 L1-PCA with quantum annealing

Ian Tomeo, Panagiotis (Panos P.) Markopoulos, Andreas Savakis

Proceedings Volume 13036, Big Data VI: Learning, Analytics, and Applications; 1303605 (2024) https://doi.org/10.1117/12.3015944
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Principal Component Analysis (PCA) is commonly used for dimensionality reduction, feature extraction, data denoising, and visualization. The L₁-PCA is known to confer robustness or a resistance to outliers in the data. In this paper, a new method for L₁-PCA is explored using quantum annealing hardware. To showcase performance increases as compared to other PCA types, results for a fault detection scenario are presented and the speedup of L₁-PCA using quantum annealing is demonstrated. Additionally, L₁-PCA has better fault detection rates than L₂-PCA when in the presence of outliers.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ian Tomeo, Panagiotis (Panos P.) Markopoulos, and Andreas Savakis "L1-PCA with quantum annealing", Proc. SPIE 13036, Big Data VI: Learning, Analytics, and Applications, 1303605 (10 June 2024); https://doi.org/10.1117/12.3015944

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