论文标题
将分离性非负矩阵分解应用于金融数据
Applying separative non-negative matrix factorization to extra-financial data
论文作者
论文摘要
我们在这里介绍了非负矩阵分解（NMF）方法的原始应用，以实现金融大数据。这些数据在共同变量之间以及观察结果之间存在很高的相关性。与简单的主成分分析（PCA）相比，NMF提供了共同变量和观察的相关聚类。此外，我们表明，在应用NMF之前，初始数据分离步骤进一步提高了聚类的质量。
We present here an original application of the non-negative matrix factorization (NMF) method, for the case of extra-financial data. These data are subject to high correlations between co-variables, as well as between observations. NMF provides a much more relevant clustering of co-variables and observations than a simple principal component analysis (PCA). In addition, we show that an initial data separation step before applying NMF further improves the quality of the clustering.
