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Dimension reduction using Independent Component Analysis with an application in business psychology

University of British Columbia

Independent component analysis (ICA) is used for separating a set of mixed signals into statistically independent additive subcomponents. The methodology extracts as many independent components as there are dimensions or features in the original dataset. Since not all of these components may be of importance, a few solutions have been proposed to reduce the dimension of the data using ICA. However, most of these solutions rely on prior knowledge or estimation of the number of independent components that are to be used in the model. This work proposes a methodology that addresses the problem of selecting fewer components than the original dimension of the data that best approximate the original dataset without prior knowledge or estimation of their number. The trade off between the number of independent components retained in the model and the loss of information is explored. This work presents mathematical foundations of the proposed methodology as well as the results of its application to a business psychology dataset.

Text

2017-03-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60963

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/