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A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [6] by introducing a reduced set of constraints for exact NMF. In contrast to the previous approaches,(More)
Signals transmitted through a wireless channel undergo distortion due to multi-path and Doppler shift effects which can be recovered at the receiver through channel state information (CSI). The variation of wireless channels causes outdated CSI to be used for optimization techniques. Channel prediction allows the system to adapt modulation methods to an(More)
Nonnegative matrix factorization (NMF) has been shown to be identifiable under the separability assumption, under which all the columns(or rows) of the input data matrix belong to the convex cone generated by only a few of these columns(or rows) [1]. In real applications, however, such separability assumption is hard to satisfy. Following [4] and [5], in(More)
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