Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/328848
Type: Artigo
Title: Extending NMF to blindly separate linear-quadratic mixtures of uncorrelated sources
Author: Hosseini, Shahram
Deville, Yannick
Duarte, Leonardo T.
Selloum, Ahmed
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: Separação cega de fontes
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/MLSP.2016.7738890
Address: https://ieeexplore-ieee-org.ez88.periodicos.capes.gov.br/document/7738890
Date Issue: 2016
Appears in Collections:FCA - Artigos e Outros Documentos

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