Related Experiment Video
Updated: Feb 2, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
Published on: October 16, 2018
Probabilistic Disjoint Principal Component Analysis
Carla Ferrara1, Francesca Martella1, Maurizio Vichi1
1a Department of Statistical Sciences , Sapienza University of Rome , Rome , Italy.
Abstract:
One of the most relevant problems in principal component analysis and factor analysis is the interpretation of the components/factors. In this paper, disjoint principal component analysis model is extended in a maximum-likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.
Related Concept Videos
Principal Stresses in a Beam
Analyzing principal stresses is crucial, especially in...
Principal Moments of Area
The principal moment of inertia axes are the...
Principal Stresses
Principal Stresses: Problem Solving
Components of Stress
Interestingly, the hidden cube faces also experience these stresses, equal and...
Components of Language

