Non-negative matrix factorization algorithms modeling noise distributions within the exponential family

We developed non-negative factorization algorithms based on statistical distributions which are members of the exponential family, and using multiplicative update rules. We compared in detail the performance of algorithms derived using two particular exponential family distributions, assuming either...

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Veröffentlicht in:Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.) Jg. 2005; S. 4990 - 4993
Hauptverfasser: Cheung, V.C.K., Tresch, M.C.
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 2005
Schlagworte:
Animals
Blind source separation
Electromyography
EMG
Gaussian noise
matrix factorization
multiplicative update rule
muscle synergy
Muscles
Noise robustness
Probability distribution
signal dependent noise
Signal processing
Statistical distributions
Vectors
ISBN:0780387414, 9780780387416
ISSN:1094-687X, 1557-170X
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Zusammenfassung:We developed non-negative factorization algorithms based on statistical distributions which are members of the exponential family, and using multiplicative update rules. We compared in detail the performance of algorithms derived using two particular exponential family distributions, assuming either constant variance noise (Gaussian) or signal dependent noise (gamma). These algorithms were compared on both simulated data sets and on muscle activation patterns collected from behaving animals. We found that on muscle activation patterns, which are expected to be corrupted by signal dependent noise, the factorizations identified by the algorithm assuming gamma distributed data were more robust than those identified by the algorithm assuming Gaussian distributed data
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ISBN:0780387414
9780780387416
ISSN:1094-687X
1557-170X
DOI:10.1109/IEMBS.2005.1615595