Blind Separation of Analytes in Nuclear Magnetic Resonance Spectroscopy: Improved Model for Nonnegative Matrix Factorization
نویسندگان
چکیده
We introduce improved model for sparseness constrained nonnegative matrix factorization (sNMF) of amplitude mixtures nuclear magnetic resonance (NMR) spectra into greater number of component spectra. In proposed method selected sNMF algorithm is applied to the square of the amplitude of the mixtures NMR spectra instead to the amplitude spectra itself. Afterwards, the square roots of separated squares of components spectra and concentration matrix yield estimates of the true components amplitude spectra and of concentration matrix. Proposed model
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تاریخ انتشار 2015