scholarly journals The Truth behind the Zeros: A New Approach to Principal Component Analysis of the Neuropsychiatric Inventory

2020 ◽  
pp. 1-16
Author(s):  
Kristoffer H. Hellton ◽  
Jeffrey Cummings ◽  
Audun Osland Vik-Mo ◽  
Jan Erik Nordrehaug ◽  
Dag Aarsland ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Neuropsychiatric Inventory ◽  
New Approach

scholarly journals Scaled PCA: A New Approach to Dimension Reduction

2021 ◽  
Author(s):  
Dashan Huang ◽  
Fuwei Jiang ◽  
Kunpeng Li ◽  
Guoshi Tong ◽  
Guofu Zhou
Keyword(s):  
Principal Component Analysis ◽  
Supervised Learning ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
General Factor ◽  
Macroeconomic Forecasting ◽  
New Approach ◽  
Learning Technique ◽  
The Common

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 maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.

Sign in / Sign up

Export Citation Format

Share Document