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Type: Congresso
Title: Extending Nmf To Blindly Separate Linear-quadratic Mixtures Of Uncorrelated Sources
Author: Hosseini
Shahram; Deville
Yannick; Duarte
Leonardo T.; Selloum
Abstract: This paper proposes a new constrained method, based on non-negative matrix factorization, for blindly separating linear-quadratic (LQ) mixtures of mutually uncorrelated source signals when the sources and mixing parameters are all non-negative. The uncorrelatedness of the sources is used as a regularization term in the cost function. The main advantage of exploiting uncorrelatedness in this manner is that the inversion of the mixing model, which is a difficult task in the case of determined LQ mixtures, is not required, contrary to the classical LQ methods based on independent component analysis. Experimental results using artificial data and real-world chemical data confirm the effectiveness of our method.
Subject: Blind Source Separation
Non-negative Matrix Factorization (nmf)
Linear-quadratic Mixtures
Un-correlated Sources
Editor: IEEE
New York
Citation: 2016 Ieee 26th International Workshop On Machine Learning For Signal Processing (mlsp). Ieee, p. , 2016.
Rights: fechado
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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