大数据典型相关分析的云模型方法

针对传统大数据典型相关分析（CCA, canonical correlation analysis）方法的高复杂度在面临大数据PB级数据规模时不再适应的现状，提出了一种基于云模型的大数据CCA方法。该方法在云计算架构的基础上，通过云运算将各端点云合并为中心云，并据此产生中心云滴，以中心云滴作为大数据的不确定性复原小样本，在其上施以CCA运算，中心云滴的较小数据量提高了运算效率。在真实数据集上的实验结果验证了该方法的有效性。

Canonical correlation analysis of big data based on cloud model

The complexity of traditional CCA methods is too high to meet the requirements to analyze big data due to their huge scale which is reaching the level of peta-byte. A novel approach to CCA was proposed to mine the big data by introducing the cloud model which is a brand-nowel theory about the uncertainty artificial intelligence. A distributed architecture based on cloud computing was established. All of the clouds distributing on the nodes of the distributed architecture were combined to a center cloud via cloud operation (where cloud is a synopsis of data and which is a concept coming from the cloud theory). A type of virtual sample of data called cloud drops created based on the center cloud. Finally the computing of CCA was imposed on the cloud drops. The CCA was impose on the cloud drops with less volume, which improves the efficiency. Experimental results on real data sets indicate the effectiveness of this method.