Unkel, Steffen; Hannachi, Abdel; Trendafilov, Nickolay T. and Jolliffe, Ian T.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1007/s13253-011-0055-9|
|Google Scholar:||Look up in Google Scholar|
In this paper, a new approach to independent component analysis (ICA) for three-way data is considered. The rotational freedom of the three-mode component analysis (Tucker3) model is exploited to implement ICA in one mode of the data. The performance of the proposed approach is evaluated by means of numerical experiments. An illustration with real data from atmospheric science is presented, where the first mode is spatial location, the second is time and the third is a set of different meteorological variables representing geopotential heights at various vertical pressure levels. The results show that the three-mode decomposition finds spatial patterns of climate anomalies which can be interpreted in a meteorological sense and as such gives an insightful low-dimensional representation of the data.
|Item Type:||Journal Article|
|Copyright Holders:||2011 International Biometric Society|
|Keywords:||geopotential heights; gridded climate data; independent component analysis; rotation; spatial patterns; three-way data; Tucker3 model|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
|Depositing User:||Steffen Unkel|
|Date Deposited:||15 Mar 2011 11:44|
|Last Modified:||04 Oct 2016 11:01|
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