Gerhard Pöppel

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Although non-negative matrix factorization has become a popular data analysis tool for non-negative data sets, there are still some issues remaining partly unsolved. We investigate the potential of Bayesian techniques towards the solution of two important open questions concerning uniqueness and actual number of sources underlying the data. We derive a(More)
We introduce a new Blind Source Separation Approach called binNTF which operates on tensor-valued binary datasets. Assuming that several simultaneously acting sources or elementary causes are generating the observed data, the objective of our approach is to uncover the underlying sources as well as their individual contribution to each observation with a(More)