ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 138

Colored subspace analysis

Author(s): Fabian Theis, Motoaki Kawanabe


With the advent of high-throughput data recording methods in biology and medicine, the efficient identification of meaningful subspaces within these data sets becomes an increasingly important challenge. Classical dimension reduction techniques such as principal component analysis often do not take the large statistics of the data set into account, and thereby fail if the signal space is for example of low power but meaningful in terms of some other statistics. With 'colored subspace analysis', we propose a method for linear dimension reduction that evaluates the time structure of the multivariate observations. We differentiate the signal subspace from noise by searching for a subspace of non-trivially autocorrelated data; algorithmically we perform this search by joint low-rank approximation. In contrast to blind source separation approaches we however do not require the existence of sources, so the model is applicable to any wide-sense stationary time series without restrictions. Moreover, since the method is based on second-order time structure, it can be efficiently implemented even for large dimensions. We conclude with an application to dimension reduction of functional MRI recordings.

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