On the number of principal components: A test of dimensionality based on measurements of similarity between matrices

An important problem in principal component analysis (PCA) is the estimation of the correct number of components to retain. PCA is most often used to reduce a set of observed variables to a new set of variables of lower dimensionality. The choice of this dimensionality is a crucial step for the interpretation of results or subsequent analyses, because it could lead to a loss of information

• Type: Journal
• Year: 2008
• Where: CSDA
• Authors: Stéphane Dray
• Keywords: Correct Number, CSDA 2008, Important Problem, Principal Component