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Special Graduate Schools Graduate School of Science and Technology Department of Industrial Engineering 3. Theses(Master)

An Approximation Method for Semi-Supervised Learning for Multi-Layered Networks

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dc.contributor.advisor신현정-
dc.contributor.author김명준-
dc.date.accessioned2018-11-08T08:23:54Z-
dc.date.available2018-11-08T08:23:54Z-
dc.date.issued2017-08-
dc.identifier.other25839-
dc.identifier.urihttps://dspace.ajou.ac.kr/handle/2018.oak/13585-
dc.description학위논문(석사)--아주대학교 일반대학원 :산업공학과,2017. 8-
dc.description.tableofcontentsChapter 1. Introduction Chapter 2. Background 2.1 Graph Structure and Graph Laplacian 2.2 Graph Based Semi-Supervised Learning Chapter 3. Proposed Method 3.1 Semi-Supervised Learning for Multi-Layered Networks 3.2 Revised Matrix Inversion for Multi-Layered Networks 3.3 Overview of the Proposed Method Chapter 4. Experiments 4.1 Artificial Data 4.2 Real World Problem I: Biomedical Data 4.3 Real World Problem II: Historical Data Chapter 5. Conclusion References-
dc.language.isoeng-
dc.publisherThe Graduate School, Ajou University-
dc.rights아주대학교 논문은 저작권에 의해 보호받습니다.-
dc.titleAn Approximation Method for Semi-Supervised Learning for Multi-Layered Networks-
dc.title.alternativeAn Approximation Method for Semi-Supervised Learning for Multi-Layered Networks-
dc.typeThesis-
dc.contributor.affiliation아주대학교 일반대학원-
dc.contributor.department일반대학원 산업공학과-
dc.date.awarded2017. 8-
dc.description.degreeMaster-
dc.identifier.localId788492-
dc.identifier.urlhttp://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000025839-
dc.subject.keywordSemi-supervised learning-
dc.subject.keywordMulti-layered networks-
dc.subject.keywordApproximation for semi-supervised learning-
dc.description.alternativeAbstractIn this study, we deal with multi-layered networks. In practical applications, many cases where a data set can be represented with heterogeneous sources of data that may be closely related in multi-layered structure exist. In multi-layered networks, labels in one layer can benefit inference in other layers through inter-layer connections. Many of existing works, however, are only concerned with incorporating multiple networks in parallel fashion, i.e, network integration method or multi-view learning. For multi-layered networks, one has to consider an integrative approach in vertical fashion In this thesis, we present a basic framework of graph based semi-supervised learning that can be applied to multi-layered networks. The layered structure of multiple networks, however, causes scalability issues – computational complexity and sparseness. To alleviate these problems, we propose a revised matrix inversion method consisting of Nyström method and Woodbury formula. To verify the validity of the proposed algorithm, we applied the algorithm to artificial data and two real-world problems with biomedical data and historical data. Experiments show the performance of multi-layered network surpasses that of single-layered networks and our proposed method is not only robust for approximations with Nyström method but also computationally efficient.-
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Special Graduate Schools > Graduate School of Science and Technology > Department of Industrial Engineering > 3. Theses(Master)
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