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Estimation of the Regularization Parameter of an On-Line NMF with Minimum Volume Constraint

机译:估计最小卷约束的线NMF的正则化参数

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In this paper, the estimation of the regularization parameter of the on-line Non-negative Matrix Factorization (NMF) with minimum volume constraint on sources is addressed. Adding a volume constraint in the model is important to ensure uniqueness of the solution and good data representation. However, the effectiveness of this approach is hampered by the optimal determination of the strength of minimum volume term. To solve this problem, we formulate it as a bi-objective optimization problem and three Minimum Distance Criterion (MDC) strategies are proposed and evaluated. The three strategies yield similar results but one of them in particular yields an interesting tradeoff between accuracy and computation time.
机译:在本文中,解决了对源上最小音量约束的在线非负矩阵分解(NMF)的正则化参数的估计。在模型中添加音量约束对于确保解决方案的唯一性和良好的数据表示是很重要的。然而,通过最小体积术语的强度的最佳测定,这种方法的有效性受到阻碍。为了解决这个问题,我们将其作为双目标优化问题制定,提出并评估了三种最小距离标准(MDC)策略。三种策略产生类似的结果,但其中一个特别是在准确性和计算时间之间产生有趣的权衡。

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