AJOU Central Library Repository: Semi-Supervised Learning for Hierarchical Networks

Graduate School of Ajou University Department of Artificial Intelligence 4. Theses(Ph.D)

Semi-Supervised Learning for Hierarchical Networks

Keyword: Semi-supervised learning; hierarchical networks; label propagation; network-based Gaussian process; network-based machine learning

Alternative Abstract: 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.

Appears in Collections:: Graduate School of Ajou University > Department of Artificial Intelligence > 4. Theses(Ph.D)

qrcode

AJOU Central Library Repository는 국립중앙도서관 OAK 보급사업으로 구축되었습니다.