Conference paper

Efficient String Matching Algorithms for Combinatorial Universal Denoising

Inspired by the combinatorial denoising method {\tt DUDE} \cite{WOSVW04}, we present efficient algorithms for implementing this idea for arbitrary contexts or for using it within subsequences. We also propose effective, efficient denoising error estimators so we can find the best denoising of an input sequence over different context lengths. Our methods are simple, drawing from string matching methods and radix sorting. We also present experimental results of our proposed algorithms.

    Keywords: NCCR-MICS ; NCCR-MICS/CL1


    • LICOS-CONF-2005-004

    Record created on 2006-12-11, modified on 2017-11-20

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