Canonical correlation analysis of big data based on cloud model - Jing YANG; Wen-ping LI; Jian-pei ZHANG (Tongxin xuebao)
The complexity of traditional CCA methods is too high 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 ar-chitecture based on cloud computing was established.All of the clouds distributing on the nodes of the distributed archi-tecture were combined to a center cloud via cloud operation (whe 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.Fi-nally 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.