Non‐negative matrix factorization and its application in blind sparse source separation with less sensors than sources
Copyright policy )Non‐negative matrix factorization and its application in blind sparse source separation with less sensors than sources
PurposeProposes a non‐negative matrix factorization method.Design/methodology approachPresents an algorithm for finding a suboptimal basis matrix. This is controlled by data cluster centers which can guarantee that the coefficient is very sparse. This leads to the proposition of an application of non‐matrix factorization for blind sparse source separation with less sensors than sources.FindingsTwo simulation examples reveal the validity and performance of the algorithm in this paper.Originality/valueUsing the approach in this paper, the sparse sources can be recovered even if the sources are overlapped to some degree.
Technical applications of optics and electromagnetic theory, Positive matrices and their generalizations; cones of matrices, Numerical mathematical programming methods, Detection theory in information and communication theory, Other matrix algorithms, Factorization of matrices
Technical applications of optics and electromagnetic theory, Positive matrices and their generalizations; cones of matrices, Numerical mathematical programming methods, Detection theory in information and communication theory, Other matrix algorithms, Factorization of matrices
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