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IJCNN
2006
IEEE

Generalizing Independent Component Analysis for Two Related Data Sets

11 years 7 months ago
Generalizing Independent Component Analysis for Two Related Data Sets
— 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
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Juha Karhunen, Tomas Ukkonen
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