Invariant kernel functions for pattern analysis and machine learning

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Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invariance is commonly used in conceptionally different ways. We propose a more distinguishing treatment in particular in the active field of kernel methods for machine learning and pattern analysis. Additionally, the fundamental relation of invariant kernels and traditional invariant pattern analysis by means of invariant representations will be clarified. After addressing these conceptional questions, we focus on practical aspects and present two generic approaches for constructing invariant kernels. The first approach is based on a technique called invariant integration. The second approach builds on invariant distances. In principle, our approaches support general transformations in particular covering discrete and non-group or even an infinite number of pattern-transformations. Additionally, both enable a sm...
Bernard Haasdonk, Hans Burkhardt
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where ML
Authors Bernard Haasdonk, Hans Burkhardt
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