.png){kind=icon}
返回
Semi-supervised learning for hierarchically structured networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2019.06.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 paper, we propose two versions of an algorithm, based on semi-supervised learning, for a hierarchically structured network. The naive version utilizes existing method for matrix sparseness to solve label propagation problems. In its approximate version, the loss in accuracy versus the gain in complexity is exploited by providing analyses on error bounds and complexity. The experimental results show that the proposed algorithms perform well with hierarchically structured data, and, outperform an ordinary semi-supervised learning algorithm. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Hierarchical graph integration
Hierarchical networks
Hierarchically structured networks
Semi-supervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏Multilayer graph cuts based unsupervised color-texture image segmentation using multivariate mixed student's t-distribution and regional credibility merging.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容