Abstract
A new blind source separation method for non-negative sources based on geometrical evidences of the linear mixing model is presented. We show that the proposed method is able to find the mixing matrix as well as the original sources from an observation matrix under the assumption that for every source there is at least one instance where the underlined source is active and all the others are not. One major advantage of our proposal is that the number of sources is found automatically as being the number of extreme data in a set of points. Under the assumption mentioned above, our approach outperforms two well known implementations for NNMF BSS (ALS and multiplicative update algorithms).
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Lazar, C., Nuzillard, D., Nowé, A. (2010). A New Geometrical BSS Approach for Non Negative Sources. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_66
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DOI: https://doi.org/10.1007/978-3-642-15995-4_66
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