Delmaire Gilles, Omidvar Mahmoud, Puigt Matthieu, Ledoux Frédéric, Limem Abdelhakim, Roussel Gilles, Courcot Dominique
Laboratoire LISIC-EA 4491, Université du Littoral Côte d'Opale, F-62228 Calais, France.
Laboratoire UCEIV-EA 4492, Université du Littoral Côte d'Opale, SFR CONDORCET FR CNRS 3417, F-59140 Dunkerque, France.
Entropy (Basel). 2019 Mar 6;21(3):253. doi: 10.3390/e21030253.
In this paper, we propose informed weighted non-negative matrix factorization (NMF) methods using an α β -divergence cost function. The available information comes from the exact knowledge/boundedness of some components of the factorization-which are used to structure the NMF parameterization-together with the row sum-to-one property of one matrix factor. In this contribution, we extend our previous work which partly involved some of these aspects to α β -divergence cost functions. We derive new update rules which are extendthe previous ones and take into account the available information. Experiments conducted for several operating conditions on realistic simulated mixtures of particulate matter sources show the relevance of these approaches. Results from a real dataset campaign are also presented and validated with expert knowledge.
在本文中，我们提出了使用αβ散度代价函数的信息加权非负矩阵分解（NMF）方法。可用信息来自分解的某些分量的精确知识/有界性，这些知识/有界性用于构建NMF参数化，同时还来自一个矩阵因子的行和为一的性质。在本论文中，我们将之前部分涉及这些方面的工作扩展到αβ散度代价函数。我们推导了新的更新规则，这些规则扩展了之前的规则，并考虑了可用信息。针对几种运行条件对实际模拟的颗粒物源混合物进行的实验表明了这些方法的相关性。还展示了来自真实数据集活动的结果，并通过专家知识进行了验证。
