L1-PCA with Quantum Annealing

Principal Component Analysis (PCA) is commonly used for dimensionality reduction, feature extraction, data denoising, and visualization. The L-1-PCA is known to confer robustness or a resistance to outliers in the data. In this paper, a new method for L-1-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 L-1-PCA using quantum annealing is demonstrated. Additionally, L-1-PCA has better fault detection rates than L-2-PCA when in the presence of outliers.