Presentation + Paper
10 June 2024 L1-PCA with quantum annealing
Author Affiliations +
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.
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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KEYWORDS
Principal component analysis

Quantum annealing

Quantum communications

Quantum hardware

Quantum numbers

Matrices

Singular value decomposition

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