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arXiv · 1205.6935

Signal Enhancement as Minimization of Relevant Information Loss

Bernhard C. Geiger·Gernot Kubin
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Abstract

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.

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Bernhard C. Geiger, Gernot Kubin. 2012-05-31. Signal Enhancement as Minimization of Relevant Information Loss. https://arxiv.org/abs/1205.6935

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