Signal Enhancement as Minimization of Relevant Information Loss
Conference: SCC 2013 - 9th International ITG Conference on Systems, Communication and Coding
01/21/2013 - 01/24/2013 at München, Deutschland
Proceedings: SCC 2013
Pages: 6Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Geiger, Bernhard C.; Kubin, Gernot (Signal Processing and Speech Communication Laboratory, Graz University of Technology, Austria)
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.