.png){kind=icon}
返回
Fast Multi-View Semi-Supervised Learning With Learned Graph
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2020.2978844.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-view semi-supervised learning (SSL) has attracted great attention due to its effectiveness in information utilization of multiple views and labeled and unlabeled data to solve practical problems. However, most existing methods exhibit high computational complexity. Effective integration of the information on different views to achieve enhanced performance remains a challenging task. In this study, we combine an anchor-based approach with multi-view semi-supervised learning to address these problems. A novel multi-view SSL method called fast multi-view SSL (FMSSL) based on learned graph is proposed. Starting from the affinity graphs constructed by using an anchor-based strategy, FMSSL learns an optimal multi-view consensus graph by using feature and label information. The learned graph can jointly consider the relation of multiple views to approximate the manifold structure. The learned graph is then introduced into the SSL model as the weight matrix of a bipartite graph to simultaneously perform separate classification on the original samples and anchors. Accordingly, multi-view SSL can be efficiently performed, and the computational complexity can be significantly reduced. We propose an effective algorithm to optimize the objective function. Extensive experimental results on different real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
Keyword:
Multi-view semi-supervised learning
computational complexity
anchor-based strategy
bipartite graph
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:
10.4
论文数:
6.9K
被引数:
3.2W
机构
引用论文
Prediction of Pregabalin-Mediated Pain Response by Severity of Sleep Disturbance in Patients with Painful Diabetic Neuropathy and Post-Herpetic Neuralgia疼痛性糖尿病神经病变和带状疱疹后神经痛患者睡眠障碍严重程度预测普瑞巴林介导的疼痛反应
PREAI
分享收藏Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification用于图像聚类和半监督分类的自动加权多视图学习
PREAI
分享收藏Multi-View Spectral Clustering via Integrating Global and Local Graphs通过集成全局和局部图的多视图谱聚类.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容