We present a nonlinear generalization of Canonical Correlation Analysis (CCA) to find related structure in multiple data sets. The new method allows to analyze an arbitrary number of data sets, and the extracted features capture higher-order statistical dependencies. The features are independent components that are coupled across the data sets. The coupling takes the form of coactivation (dependencies of variances). We validate the new method on artificial data, and apply it to natural images and brain imaging data. © 2011 Springer-Verlag.
CITATION STYLE
Gutmann, M. U., & Hyvärinen, A. (2011). Extracting coactivated features from multiple data sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6791 LNCS, pp. 323–330). https://doi.org/10.1007/978-3-642-21735-7_40
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