Various Non-negative Matrix factorization (NMF) based methods add new terms to the cost function to adapt the model to specific tasks, such as clustering, or to preserve some structural properties in the reduced space (e.g., local invariance). The added term is mainly weighted by a hyper-parameter to control the balance of the overall formula to guide the optimization process towards the objective. The result is a parameterized NMF method. However, NMF method adopts unsupervised approaches to estimate the factorizing matrices. Thus, the ability to perform prediction (e.g. classification) using the new obtained features is not guaranteed. The objective of this work is to design an evolutionary framework to learn the hyper-parameter of the parameterized NMF and estimate the factorizing matrices in a supervised way to be more suitable for classification problems. Moreover, we claim that applying NMF-based algorithms separately to different class-pairs instead of applying it once to the whole dataset improves the effectiveness of the matrix factorization process. This results in training multiple parameterized NMF algorithms with different balancing parameter values. A cross-validation combination learning framework is adopted and a Genetic Algorithm is used to identify the optimal set of hyper-parameter values. The experiments we conducted on both real and synthetic datasets demonstrated the effectiveness of the proposed approach.
翻译:以各种非负矩阵因子化(NMF)为基础的方法增加了成本功能的新术语,使模型适应特定任务,例如集群,或保存空间缩小(例如,局部差异)中的某些结构属性。添加的术语主要用超参数加权,以控制总体公式的平衡,指导优化进程达到目标。结果是一种参数化的NMF方法。但是,NMF方法采用未经监督的方法来估计系数化矩阵。因此,使用新获得的特性进行预测(如分类)的能力得不到保证。这项工作的目标是设计一个进化框架,以学习参数化NMF的超参数,并以监督的方式估计因子化矩阵,以更适合分类问题。此外,我们声称,将基于NMF的算法分别应用于不同的等级,而不是一次应用到整个数据集,可以提高矩阵因子化方法的有效性。在培训具有不同平衡参数值的多参数化NMF算法(如分类)方面,这项工作的目标是设计一个进化框架,以学习参数的超参数性参数,并以监督的方式估计成因子矩阵矩阵矩阵矩阵矩阵矩阵,同时确定所展示的遗传学系。采用的最佳模型。