Semi-Supervised Learning for Hierarchical Networks
- 주제(키워드) Semi-supervised learning , network-based machine learning , hierarchical networks , label propagation , network-based Gaussian process
- 주제(DDC) 006.31
- 발행기관 아주대학교
- 지도교수 신현정
- 발행년도 2021
- 학위수여년월 2021. 8
- 학위명 박사
- 학과 및 전공 일반대학원 인공지능학과
- 실제URI http://www.dcollection.net/handler/ajou/000000031197
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
A set of data can be obtained from different hierarchical levels in diverse domains, such as multi-levels of genome data in omics, domestic/global indicators in finance, ancestors/descendants in phylogenetics, genealogy, and sociology. Such layered structures are often represented as a hierarchical network. If a set of different data is arranged in such a way, then one can naturally devise a network-based learning algorithm so that information in one layer can be propagated to other layers through interlayer connections. Incorporating individual networks in layers can be considered as an integration in a serial/vertical manner in contrast with parallel integration for multiple independent networks. The hierarchical integration induces several problems on computational complexity, sparseness, and scalability because of a huge-sized matrix. In this dissertation, we propose semi-supervised framework of classification and regression for a hierarchically structured network. The proposed frameworks consists of naive and approximate versions, where trade-off between performance and time complexity exists. Furthermore, we show empirical performances of hierarchical network on various task along with some real-world applications including historical faction identification, disease co-occurrence prediction, and key gene identification for Dementia.
more목차
1. Introduction 1
2. Representation of hierarchical networks 6
3. Semi-supervised learning for hierarchical networks: Classification 11
4. Semi-supervised learning for hierarchical networks: Regression 27
5. Applications of hierarchical networks 37
6. Conclusion 75
References 80
