2006 Seventh Mexican International Conference on Computer Science (2006)

San Luis Potosi, Mexico

Sept. 18, 2006 to Sept. 22, 2006

ISSN: 1550-4069

ISBN: 0-7695-2666-7

pp: 19-26

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ENC.2006.15

Arturo Berrones , Universidad Aut?onoma de Nuevo Leon, Mexico

ABSTRACT

A new approach to the problem of noise reduction in signals composed by superpositions of basis functions is proposed. The method is based on interpreting the components of signal models as nodes in a sparsely connected network of overlaps (scalar products). Every point in the data sample expresses an overlap. Networks of this kind, in which nodes carry information by means of vectors, define a knowledge network, a recently introduced concept in the field of statistical physics. Previous results on the statistical properties of knowledge networks are generalized to noise reduction and its shown that is possible to extract important hidden quantities. In particular, an algorithm capable to give estimates of the unkown number of degrees of freedom in signal models is constructed and tested.

INDEX TERMS

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CITATION

A. Berrones, "Filtering by Sparsely Connected Networks Under the Presence of Strong Additive Noise,"

*2006 Seventh Mexican International Conference on Computer Science(ENC)*, San Luis Potosi, Mexico, 2006, pp. 19-26.

doi:10.1109/ENC.2006.15

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