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Extracting coactivated features from multiple data sets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2011
Subtitle of host publication21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I
EditorsT Honkela
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Pages323-330
Number of pages8
ISBN (Electronic)978-3-642-21735-7
ISBN (Print)978-3-642-21734-0
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume6791
ISSN (Print)0302-9743

Abstract

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.

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