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This work is devoted to the factorization of an observation matrix into additive factors, respectively a contribution matrix G and a profile matrix F which enable to identify many pollution sources. The search for G and F is achieved through Non-negative Matrix Factorization techniques which alternatively look for the best updates on G and F.These methods are sensitive to noise and initialization,...
In our recent work, we introduced a constrained weighted Non-negative Matrix Factorization (NMF) method using a β-divergence cost function. We assumed that some components of the factorization were known and were used to inform our NMF algorithm. In this paper, we are provided some intervals of possible values for some factorization components. We thus introduce an extended version of our previous...
In this paper, we propose two weighted Non-negative Matrix Factorization (NMF) methods using a β-divergence cost function. This divergence is used as a dissimilarity measure which can be tuned by the parameter β. The weights allow to deal with the uncertainty associated to each data sample. Our first approach consists of generalizing weighted NMF methods proposed with specific divergences or norms...
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