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Proceedings Paper

Non-negative factorization of non-negative matrices
Author(s): John Gruninger
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Paper Abstract

A new non-negative factorization method has been developed. The method is based on the concept of non-negative rank (NNR). Bounds for the NNR of certain non-negative matrices are determined relative to the rank of the matrix, with the lower bound being equal to the rank. The method requires that the data matrix be non-negative and have a large first singular value. Unlike other non-negative factorization methods, the approach does not assume or require that the factors be linearly independent and no assumption of statistical independence is required. The rank of the matrix provides the number of linearly independent components present in the data while the non-negative rank provides the number of non-negative independent components present in the data. The method is described and illustrated in application to hyperspectral data sets.

Paper Details

Date Published: 24 October 2007
PDF: 9 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480H (24 October 2007); doi: 10.1117/12.738381
Show Author Affiliations
John Gruninger, Spectral Sciences, Inc. (United States)


Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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