Abstract
In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.
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Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 40, 788–791 (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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Stadlthanner, K., Theis, F.J., Puntonet, C.G., Lang, E.W. (2005). Extended Sparse Nonnegative Matrix Factorization. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_31
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DOI: https://doi.org/10.1007/11494669_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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