Linear dimension reduction based on the fourth-order cumulant tensor

Authors:
M. Kawanabe
Affiliations:
Fraunhofer FIRST.IDA, Berlin, Germany
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

In high dimensional data analysis, finding non-Gaussian components is an important preprocessing step for efficient information processing. By modifying the contrast function of JADE algorithm for Independent Component Analysis, we propose a new linear dimension reduction method to identify the non-Gaussian subspace based on the fourth-order cumulant tensor. A numerical study demonstrates the validity of our method and its usefulness for extracting sub-Gaussian structures.