.png){kind=icon}
返回
Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation
.png){kind=icon}
OA
AI
DOI：10.1016/j.neunet.2021.11.015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph construction plays an essential role in graph-based label propagation since graphs give some information on the structure of the data manifold. While most graph construction methods rely on predefined distance calculation, recent algorithms merge the task of label propagation and graph construction in a single process. Moreover, the use of several descriptors is proved to outperform a single descriptor in representing the relation between the nodes. In this article, we propose a Multiple-View Consistent Graph construction and Label propagation algorithm (MVCGL) that simultaneously constructs a consistent graph based on several descriptors and performs label propagation over unlabeled samples. Furthermore, it provides a mapping function from the feature space to the label space with which we estimate the label of unseen samples via a linear projection. The constructed graph does not rely on a predefined similarity function and exploits data and label smoothness. Experiments conducted on three face and one handwritten digit databases show that the proposed method can gain better performance compared to other graph construction and label propagation methods. (C) 2021 The Author(s). Published by Elsevier Ltd.
Keyword:
Multi-view semi-supervised classification
Information fusion
Graph-based data smoothness
Graph construction
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:
6.3
论文数:
8.2K
被引数:
3.0W
机构
引用论文
分享收藏Dynamic graph fusion label propagation for semi-supervised multi-modality classification面向半监督多模态分类的动态图融合标签传播.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Structured optimal graph based sparse feature extraction for semi-supervised learningSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Similarity network fusion for aggregating data types on a genomic scale用于在基因组规模上聚合数据类型的相似性网络融合NATURE METHODS
NATURE METHODSIF32.1
PREAI
分享收藏
查看更多内容