Scaled PCA: A new approach to dimension reduction

• Main Authors: HUANG, Dashan, JIANG, Fuwei, LI, Kunpeng, TONG, Guoshi, ZHOU, Guofu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Forecasting; PCA; Big Data; Dimension Reduction; Machine Learning; Management Sciences and Quantitative Methods
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/6924; https://ink.library.smu.edu.sg/context/lkcsb_research/article/7923/viewcontent/SSRN_id3358911.pdf
• Institution: Singapore Management University

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximize...