Noisy independent component analysis as a method of rotating the factor scores

Unkel, Steffen and Trendafilov, Nickolay T (2007). Noisy independent component analysis as a method of rotating the factor scores. In: Davies, Mike E; James, Christopher J; Abdallah, Samer A and Plumbley, Mark D eds. Independent Component Analysis and Signal Separation: 7th International Conference, ICA 2007, London, UK, September 9-12, 2007. Proceedings. Lecture Notes in Computer Science (4666). Berlin: Springer, pp. 810–817.

DOI: https://doi.org/10.1007/978-3-540-74494-8

URL: http://www.springerlink.com/content/v2225132802r15...

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.

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