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Iterative Denoising for Cross-Corpus Discovery

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COMPSTAT 2004 — Proceedings in Computational Statistics

Abstract

We consider the problem of statistical pattern recognition in a heterogeneous, high-dimensional setting. In particular, we consider the search for meaningful cross-category associations in a heterogeneous text document corpus. Our approach involves “iterative denoising ” — that is, iteratively extracting (corpus-dependent) features and partitioning the document collection into sub-corpora. We present an anecdote wherein this methodology discovers a meaningful cross-category association in a heterogeneous collection of scientific documents.

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Author information

Authors and Affiliations

  1. AlgoTek, Inc., 3811 N. Fairfax Dr., Suite 700, USA

    Carey E. Priebe, Edward J. Wegman, Diego A. Socolinsky, Ken W. Church, Roland Guglielmi, Ronald R. Coifman, Dekang Lin, Marc Q. Jacobs & Anna Tsao

  2. NSWCDD B10, Dahlgren, VA, USA

    David J. Marchette & Jeffrey L. Solka

  3. Johns Hopkins U., Balt., MD, USA

    Youngser Park & Damianos Karakos

  4. DARPA, Arlington, VA, 22203, USA

    Dennis M. Healy

Authors
  1. Carey E. Priebe
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  2. David J. Marchette
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  3. Youngser Park
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  4. Edward J. Wegman
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  5. Jeffrey L. Solka
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  6. Diego A. Socolinsky
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  7. Damianos Karakos
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  8. Ken W. Church
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  9. Roland Guglielmi
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  10. Ronald R. Coifman
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  11. Dekang Lin
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  12. Dennis M. Healy
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  13. Marc Q. Jacobs
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  14. Anna Tsao
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Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Physics Department of Statistics and Probability, Charles University, Sokolovská 83, 18675, Prague 8 - Karlin, Czech Republic

    Jaromir Antoch

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© 2004 Springer-Verlag Berlin Heidelberg

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Priebe, C.E. et al. (2004). Iterative Denoising for Cross-Corpus Discovery. In: Antoch, J. (eds) COMPSTAT 2004 — Proceedings in Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2656-2_31

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  • DOI: https://doi.org/10.1007/978-3-7908-2656-2_31

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1554-2

  • Online ISBN: 978-3-7908-2656-2

  • eBook Packages: Springer Book Archive

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