On the Number of Independent Components: An Adjusted Coefficient of Determination based Approach


Abstract


Independent Component Analysis (ICA) is a comparatively new statisticaland computational technique to find hidden components from multivariate statistical data. The technique is also employed as a tool for dimension reduction for efficient data analysis. Reduction in dimensions can be done byassigning ranks to the independent components in some appropriate way and then restricting the data analysis to certain high ranking components only.The problem of determining the number of high ranked ICs that should be retained is the main objective of this paper. A method based upon adjusted coefficient of determination is proposed for the purpose. The performance of the proposed method is demonstrated through experimental evaluation on real-world financial time series data.

DOI Code: 10.1285/i20705948v14n1p13

Keywords: Adjusted coefficient of determination; dimension reduction; financial time series; independent component analysis; multidimensional data; big data analytics

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