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2007

A novel kernel-based method for local pattern extraction in random process signals

8 years 11 months ago
A novel kernel-based method for local pattern extraction in random process signals
In many applications, one is interested to detect certain patterns in random process signals. We consider a class of random process signals which contain sub similarities at random positions representing the texture of an object. Those repetitive parts may occur in speech, musical pieces and sonar signals. We suggest a warped time resolved spectrum kernel for extracting the subsequence similarity in time series in general, and as an example in biosonar signals. Having a set of those kernels for similarity extraction in different size of subsequences, we propose a new method to find an optimal linear combination of those kernels. We formulate the optimal kernel selection via maximizing the Kernel Fisher Discriminant criterion (KFD) and use Mesh Adaptive Direct Search (MADS) method to solve the optimization problem. Our method is used for biosonar landmark classification with promising results. Key words: Time-resolved spectrum kernel, SVM, Fisher discriminant, Mesh adaptive direct s...
Majid Beigi, Andreas Zell
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Majid Beigi, Andreas Zell
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