We introduce in this paper methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. The basic idea is to generalize cross-correlation analysis for taking into account higher-order statistics. We propose independent component analysis (ICA) type extensions for the singular value decomposition of the cross-correlation matrix. They extend cross-correlation analysis in a similar manner as ICA extends standard principal component analysis for covariance matrices. We present experimental results demonstrating the usefulness of the proposed methods both for artificially generated data and for a cryptographic problem.
['Juha Karhunen', 'Tomas Ukkonen']
Generalizing Independent Component Analysis for Two Related Data Sets