Abstract
We propose a determinant criterion to constrain the solutions of non-negative matrix factorization problems and achieve unique and optimal solutions in a general setting, provided an exact solution exists. We demonstrate with illustrative examples how optimal solutions are obtained using our new algorithm detNMF and discuss the difference to NMF algorithms imposing sparsity constraints.
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Schachtner, R., Pöppel, G., Tomé, A.M., Lang, E.W. (2009). Minimum Determinant Constraint for Non-negative Matrix Factorization. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_14
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DOI: https://doi.org/10.1007/978-3-642-00599-2_14
Publisher Name: Springer, Berlin, Heidelberg
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