Robust Non-Negative Matrix Factorization for Multispectral Data with Sparse Prior
نویسندگان
چکیده
In this work, we study Non-Negative Matrix Factorization (NMF) and compare standard algorithms with an extension to NMF of a Blind Source Separation algorithm using sparsity, Generalized Morphological Component Analysis (GMCA). We also develop a more robust version of GMCA handling more precisely the priors through sub-iterations, which we call rGMCA. We present preliminary results showing GMCA is well suited to solve this kind of problem and in particular that the decreasing threshold it uses is helpful to disambiguate the sources. We also show that rGMCA is more robust to correlation between the sources.
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