PuSH - Publication Server of Helmholtz Zentrum München: Colored subspace analysis.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

Theis, F.J.* ; Kawanabe, M.*

Colored subspace analysis.

Lect. Notes Comput. Sc. 4666, 121-128 (2007)

DOI

Open Access Green as soon as Postprint is submitted to ZB.

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]