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Published in:
2010 | OriginalPaper | Chapter
Second-Order Source Separation Based on Prior Knowledge Realized in a Graph Model
Authors : Florian Blöchl, Andreas Kowarsch, Fabian J. Theis
Published in: Latent Variable Analysis and Signal Separation
Publisher: Springer Berlin Heidelberg
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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.