Extended Sparse Nonnegative Matrix Factorization
نویسندگان
چکیده
In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated. 1 Matrix Factorization and Blind Source Separation Often when it comes to analyze recorded observations, a suitable data representation is sought. One way of finding such a data representation is matrix factorization, where the m × T observation matrix X is decomposed into a m× n matrix W and a n× T matrix H
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