International audienceIn 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 work combining an improved divergence expression and some matrix normalizationswhile using the known / bounded information. Some experiments on simulated mixtures of particulate matter sources show the relevance of these approaches
Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when ...
An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal s...
A novel unsupervised machine learning algorithm for single channel source separation is presented. T...
International audienceIn this paper, we propose two weighted Non-negative Matrix Factorization (NMF)...
International audienceIn this paper, we propose informed weighted non-negative matrix factorization ...
Les méthodes de NMF permettent la factorisation aveugle d'une matrice non-négative X en le produit X...
Source apportionment for air pollution may be formulated as a NMF problem by decomposing the data ma...
International audienceSource apportionment is usually tackled with blind Positive/Non-negative Matri...
NMF methods aim to factorize a non-negative observation matrix X as the product X = G·F between twon...
International audienceIn a previous work, we proposed an informed Non-negative Matrix Factorization ...
NMF methods aim to factorize a non negative observation matrix X as the product X = G.F between two ...
This paper proposes a new constrained method, based on non-negative matrix factorization, for blindl...
à paraître dans Neural ComputationThis paper describes algorithms for nonnegative matrix factorizati...
In this paper we develop several algorithms for non-negative matrix factorization (NMF) in applicati...
International audienceSpectral decomposition by nonnegative matrix factorisation (NMF) has become st...
Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when ...
An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal s...
A novel unsupervised machine learning algorithm for single channel source separation is presented. T...
International audienceIn this paper, we propose two weighted Non-negative Matrix Factorization (NMF)...
International audienceIn this paper, we propose informed weighted non-negative matrix factorization ...
Les méthodes de NMF permettent la factorisation aveugle d'une matrice non-négative X en le produit X...
Source apportionment for air pollution may be formulated as a NMF problem by decomposing the data ma...
International audienceSource apportionment is usually tackled with blind Positive/Non-negative Matri...
NMF methods aim to factorize a non-negative observation matrix X as the product X = G·F between twon...
International audienceIn a previous work, we proposed an informed Non-negative Matrix Factorization ...
NMF methods aim to factorize a non negative observation matrix X as the product X = G.F between two ...
This paper proposes a new constrained method, based on non-negative matrix factorization, for blindl...
à paraître dans Neural ComputationThis paper describes algorithms for nonnegative matrix factorizati...
In this paper we develop several algorithms for non-negative matrix factorization (NMF) in applicati...
International audienceSpectral decomposition by nonnegative matrix factorisation (NMF) has become st...
Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when ...
An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal s...
A novel unsupervised machine learning algorithm for single channel source separation is presented. T...