Canonical correlation analysis of big data based on cloud model

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 uncertaint...

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Main Authors: Jing YANG, Wen-ping LI, Jian-pei ZHANG
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2013-10-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.10.015/
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author Jing YANG
Wen-ping LI
Jian-pei ZHANG
author_facet Jing YANG
Wen-ping LI
Jian-pei ZHANG
author_sort Jing YANG
collection DOAJ
description 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.
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institution Kabale University
issn 1000-436X
language zho
publishDate 2013-10-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-b4c7fe0924da42dda1c3f45e020a19aa2025-01-14T06:41:30ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2013-10-013412113459675825Canonical correlation analysis of big data based on cloud modelJing YANGWen-ping LIJian-pei ZHANGThe 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.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.10.015/big datacanonical correlation analysis (CCA)cloud modelcloud operationcloud computing
spellingShingle Jing YANG
Wen-ping LI
Jian-pei ZHANG
Canonical correlation analysis of big data based on cloud model
Tongxin xuebao
big data
canonical correlation analysis (CCA)
cloud model
cloud operation
cloud computing
title Canonical correlation analysis of big data based on cloud model
title_full Canonical correlation analysis of big data based on cloud model
title_fullStr Canonical correlation analysis of big data based on cloud model
title_full_unstemmed Canonical correlation analysis of big data based on cloud model
title_short Canonical correlation analysis of big data based on cloud model
title_sort canonical correlation analysis of big data based on cloud model
topic big data
canonical correlation analysis (CCA)
cloud model
cloud operation
cloud computing
url http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.10.015/
work_keys_str_mv AT jingyang canonicalcorrelationanalysisofbigdatabasedoncloudmodel
AT wenpingli canonicalcorrelationanalysisofbigdatabasedoncloudmodel
AT jianpeizhang canonicalcorrelationanalysisofbigdatabasedoncloudmodel