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• W58408472 abstract "Nonnegative Matrix Factorization (NMF) is an unsupervised learning method that has been already applied to many applications of spectral signal unmixing. However, its efficiency in some applications strongly depends on optimization algorithms used for estimating the underlying nonnegatively constrained subproblems. In this paper, we attempt to tackle the optimization tasks with the inexact Interior-Point (IP) algorithm that has been successfully applied to image deblurring [S. Bonettini, T. Serafini, 2009]. The experiments demonstrate that the proposed NMF algorithm considerably outperforms the well-known NMF algorithms for blind unmixing of the Raman spectra." @default.
• W58408472 created "2016-06-24" @default.
• W58408472 creator A5022656662 @default.
• W58408472 date "2012-01-01" @default.
• W58408472 modified "2023-09-26" @default.
• W58408472 title "Spectral Signal Unmixing with Interior-Point Nonnegative Matrix Factorization" @default.
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• W58408472 doi "https://doi.org/10.1007/978-3-642-33269-2_9" @default.
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