Paper No: 9
Noisy Independent Component Analysis as a Method of Rotating the Factor Scores
Author(s): Steffen Unkel, Nickolay Trendafilov
Abstract
Noisy independent component analysis (ICA) is viewed as a method
of factor rotation in exploratory factor analysis (EFA). Starting
from an initial EFA solution, rather than rotating the loadings
towards simplicity, the factors are rotated orthogonally towards
independence. An application to Thurstone's box problem in
psychometrics is presented using a new data matrix containing
measurement error. Results show that the proposed rotational
approach to noisy ICA recovers the components
used to generate the mixtures quite accurately and also produces simple loadings. |
