Independent Component Analysis Based on Information Bottleneck

dc.contributor.authorKe, Qiao
dc.contributor.authorZhang, Jiangshe
dc.contributor.authorSrivastava, Hari M.
dc.contributor.authorWei, Wei
dc.contributor.authorChen, Guang-Sheng
dc.date.accessioned2017-10-23T17:57:15Z
dc.date.available2017-10-23T17:57:15Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.description.abstractThe paper is mainly used to provide the equivalence of two algorithms of independent component analysis (ICA) based on the information bottleneck (IB). In the viewpoint of information theory, we attempt to explain the two classical algorithms of ICA by information bottleneck. Furthermore, via the numerical experiments with the synthetic data, sonic data, and image, ICA is proved to be an edificatory way to solve BSS successfully relying on the information theory. Finally, two realistic numerical experiments are conducted via FastICA in order to illustrate the efficiency and practicality of the algorithm as well as the drawbacks in the process of the recovery images the mixing images.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis investigation was supported by National Basic Research Program of China (973 Program) under Grant no. 2013CB329404, the Major Research Project of the National Natural Science Foundation of China under Grant no. 912300101, the National Natural Science Foundation of China under Grant no. 61075006, the Key Project of the National Natural Science Foundation of China under Grant no. 111311006, the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program no. 2013JK1139), the China Postdoctoral Science Foundation (no. 2013M542370), and the Specialized Research Fund for the Doctoral Program of Higher Education of the People’s Republic of China (Grant no. 20136118120010).en_US
dc.identifier.citationKe, Q., Zhang, J., Srivastava, H.M., Wei, W., & Chen, G. (2015). Independent component analysis based on information bottleneck. Abstract and Applied Analysis, Vol. 2015, 8 pages. Article ID 386201.en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2015/386201
dc.identifier.urihttp://hdl.handle.net/1828/8716
dc.language.isoenen_US
dc.publisherAbstract and Applied Analysisen_US
dc.titleIndependent Component Analysis Based on Information Bottlenecken_US
dc.typeArticleen_US

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