Axes that matter: PCA with a difference

Brian Huge and Antoine Savine

Papers from arXiv.org

Abstract: We extend the scope of differential machine learning and introduce a new breed of supervised principal component analysis to reduce dimensionality of Derivatives problems. Applications include the specification and calibration of pricing models, the identification of regression features in least-square Monte-Carlo, and the pre-processing of simulated datasets for (differential) machine learning.

Date: 2025-03, Revised 2025-03
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