Identifying the Informational/Signal Dimension in Principal Component Analysis

• Published in: Mathematics (Basel) Vol. 6; no. 11; p. 269
• Main Authors: Camiz, Sergio, Pillar, Valério
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20.11.2018
• Subjects: Comparative studies; Data analysis; Datasets; Eigenvalues; Error correction; Error detection; Food science; Identification methods; Methods; Multidimensional data; Noise; Principal Component Analysis; Principal components analysis; rules comparison; simulated data; Statistical methods; stopping rules

The identification of a reduced dimensional representation of the data is among the main issues of exploratory multidimensional data analysis and several solutions had been proposed in the literature according to the method. Principal Component Analysis (PCA) is the method that has received the larg...