Non‐negative matrix factorization and its application in blind sparse source separation with less sensors than sources

Proposes a non‐negative matrix factorization method.

Presents 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.

Two simulation examples reveal the validity and performance of the algorithm in this paper.

Using the approach in this paper, the sparse sources can be recovered even if the sources are overlapped to some degree.