In my presentation I will review the methodology of Independent Component Analysis (ICA) and propose a method of dimension reduction using ICA. ICA is used for separating 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 these components may be of importance, a few solutions have been proposed to reduce the dimension of the data using ICA, most of which rely on prior knowledge or estimation of the number of independent components that are to be used in the model. In my talk I will discuss a methodology that addresses the problem of dimension reduction that best approximates the original dataset without the prior knowledge or estimation of the number of components to be retained.