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Second-order source separation based on prior knowledge realized in a graph model.
In: Proceedings (Latent variable analysis and signal separation : 9th international conference, 27-30 September 2010, St. Malo, France). Berlin [u.a.]: Springer, 2010. 434-441 (Lecture Notes Comp. Sci. ; 6365)
Matrix factorization techniques provide efficient tools for the detailed analysis of large-scale biological and biomedical data. While underlying algorithms usually work fully blindly, we propose to incorporate prior knowledge encoded in a graph model. This graph introduces a partial ordering in data without intrinsic (e.g. temporal or spatial) structure, which allows the definition of a graph-autocorrelation function. Using this framework as constraint to the matrix factorization task we develop a second-order source separation algorithm called graph-decorrelation algorithm (GraDe). We demonstrate its applicability and robustness by analyzing microarray data from a stem cell differentiation experiment.
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Publikationstyp Artikel: Sammelbandbeitrag/Konferenzbeitrag
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
ISBN 364215994X
Konferenztitel Latent variable analysis and signal separation : 9th international conference
Konferzenzdatum 27-30 September 2010
Konferenzort St. Malo, France
Konferenzband Proceedings
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 6365, Heft: , Seiten: 434-441 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Begutachtungsstatus nicht peer-reviewed