In this paper we propose to use ICA as a dimension reduction technique for microarray data. All microarray studies present a dimensionality challenge to the researcher: the number of dimensions (genes/spots on the microarray) is many times larger than the number of samples, or arrays. Any subsequent analysis must deal with this dimensionality problem by either reducing the dimension of the data, or by incorporating some assumptions in the model that effectively regularize the solution. In this paper we propose to use the ICA approach with a regularized whitening technqiue to reduce the dimension to a small set of independent sources or latent variables, which then can be used in downstream analysis. The elements of the mixing matrix can themselves be investigated to gain more understanding about the genetic underpinnings of the process that generated the data. While a number of researchers have proposed ICA as a model for the microarray data, this paper is different in an important aspect: we focus on ICA as a dimension reduction step which leads us to the generative model formulation that applies the ICA in an opposite way to most other proposals in this field.
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