Axes that matter: PCA with a difference

Differential PCA is introduced to reduce the dimensionality in derivative pricing problems

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Brian Huge and Antoine Savine extend differential machine learning and introduce a new breed of supervised principal component analysis to reduce the dimensionality of derivatives problems, with applications including the specification and calibration of pricing models, identification of regression features in least-squares Monte Carlo and preprocessing simulated datasets for (differential) machine learning

Giles and Glasserman’s ‘Smoking adjoints’ paper

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