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Titel:

Colored subspace analysis: Dimension reduction based on a signal's autocorrelation structure

Autor(en):
Theis, F. J.
Abstract:
Identifying relevant signals within high-dimensional observations is an important preprocessing step for efficient data analysis. However, many classical dimension reduction techniques such as principal component analysis do not take the often rich statistics of real-world data into account, and thereby fail if for example the signal space is of low power but meaningful in terms of some other statistics. With "colored subspace analysis," we propose a method for linear dimension reduction that ev...     »
Stichworte:
Blind signal processing dimension reduction independent component analysis non-Gaussian component analysis principal component analysis
Zeitschriftentitel:
IEEE Trans. Circuits Syst. I-Regul. Pap.
Jahr:
2010
Band / Volume:
57
Heft / Issue:
7
Seitenangaben Beitrag:
1463-1474
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