Factor analysis as data matrix decomposition: a new approach for quasi-sphering in noisy ICA

Unkel, Steffen and Trendafilov, Nickolay (2009). Factor analysis as data matrix decomposition: a new approach for quasi-sphering in noisy ICA. In: Adali, Tülay; Jutten, Christian; Romano, João MarcosTravassos and Barros, Allan Kardec eds. Independent Component Analysis and Signal Separation: 8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009. Proceedings. Lecture Notes in Computer Science (5441). Berlin: Springer, pp. 163–170.

DOI: https://doi.org/10.1007/978-3-642-00599-2_21

URL: http://www.springerlink.com/content/149164n22637l0...

Abstract

In this paper, a new approach for quasi-sphering in noisy ICA by means of exploratory factor analysis (EFA) is introduced. The EFA model is considered as a novel form of data matrix decomposition. By factoring the data matrix, estimates for all EFA model parameters are obtained simultaneously. After the preprocessing, an existing ICA algorithm can be used to rotate the sphered factor scores towards independence. An application to climate data is presented to illustrate the proposed approach.

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