@inbook{b7a60d66d55141f2a47e264ec5800922,

title = "A clustering approach for blind source separation with more sources than mixtures",

abstract = "In this paper, blind source separation is discussed with more sources than mixtures when the sources are sparse. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. The mixing matrix can be estimated by using a clustering approach which is described by the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. After the mixing matrix is estimated, the sources can be obtained by solving a linear programming problem. The techniques we present here can be extended to the blind separation of more sources than mixtures with a Gaussian noise.",

author = "Zhenwei Shi and Huanwen Tang and Yiyuan Tang",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2004",

doi = "10.1007/978-3-540-28647-9_112",

language = "English",

isbn = "3540228411",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

pages = "684--689",

editor = "Fuliang Yin and Chengan Guo and Jun Wang",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}