Signal Enhancement as Minimization of Relevant Information Loss

Konferenz: SCC 2013 - 9th International ITG Conference on Systems, Communication and Coding
21.01.2013 - 24.01.2013 in München, Deutschland

Tagungsband: SCC 2013

Seiten: 6Sprache: EnglischTyp: PDF

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Autoren:
Geiger, Bernhard C.; Kubin, Gernot (Signal Processing and Speech Communication Laboratory, Graz University of Technology, Austria)

Inhalt:
We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show that the relevant information loss can indeed vanish if the relevant information is concentrated on a lower-dimensional subspace of the input space.