Efficient string matching algorithms for combinatorial universal denoising

S. Chen, S. Diggavi, S. Dusad, S. Muthukrishnan

    Research output: Contribution to journalConference articlepeer-review


    Inspired by the combinatorial denoising method DUDE [13], 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.

    Original languageEnglish (US)
    Pages (from-to)153-162
    Number of pages10
    JournalData Compression Conference Proceedings
    StatePublished - 2005
    EventDCC 2005: Data Compression Conference - Snowbird, UT, United States
    Duration: Mar 29 2005Mar 31 2005

    ASJC Scopus subject areas

    • Computer Networks and Communications


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