Component ordering in independent component analysis based on data power
Hendrikse, A.J. and Veldhuis, R.N.J. and Spreeuwers, L.J. (2007) Component ordering in independent component analysis based on data power. In: 28th Symposium on Information Theory in the Benelux, 2425 May 2007, Enschede, The Netherlands (pp. pp. 211218).

PDF
282kB 
Abstract:  With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have
to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the data power of a component, which indicates the amount of input data variance explained by an independent component. This method resembles the eigenvalue ordering of principle components. We will demonstrate theoretically and with experiments that strong sources can be estimated with higher accuracy than weak components. Based on the selection by data power, a method is developed for estimating independent components in high dimensional spaces. A test with synthetic data shows that the new algorithm can provide higher accuracy than the usual PCA dimension reduction. 
Item Type:  Conference or Workshop Item 
Faculty:  Electrical Engineering, Mathematics and Computer Science (EEMCS) 
Research Group:  
Link to this item:  http://purl.utwente.nl/publications/64279 
Export this item as:  BibTeX EndNote HTML Citation Reference Manager 
Repository Staff Only: item control page
Metis ID: 241832