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A Unified Framework Based on Graph Consensus Term for Multiview Learning
Xiangzhu Meng1; Lin Feng2; Chonghui Guo2; Huibing Wang3; Shu Wu1
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
2022-09-12
Volume35Issue:3Pages:3964 - 3977
Abstract

In recent years, multiview learning technologies have attracted a surge of interest in the machine learning domain. However, when facing complex and diverse applications, most multiview learning methods mainly focus on specific fields rather than provide a scalable and robust proposal for different tasks. Moreover, most conventional methods used in these tasks are based on single view, which cannot be readily extended into the multiview scenario. Therefore, how to provide an efficient and scalable multiview framework is very necessary yet full of challenges. Inspired by the fact that most of the existing single view algorithms are graph-based ones to learn the complex structures within given data, this article aims at leveraging most existing graph embedding works into one formula via introducing the graph consensus term and proposes a unified and scalable multiview learning framework, termed graph consensus multiview framework (GCMF). GCMF attempts to make full advantage of graph-based works and rich information in the multiview data at the same time. On one hand, the proposed method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods; on the other hand, learned graphs can be flexibly chosen to construct the graph consensus term, which can more stably explore the correlations among multiple views. To this end, GCMF can simultaneously take the diversity and complementary information among different views into consideration. To further facilitate related research, we provide an implementation of the multiview extension for locality linear embedding (LLE), named GCMF-LLE, which can be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.

Language英语
Sub direction classification机器学习
planning direction of the national heavy laboratory智能计算与学习
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57467
Collection模式识别实验室
Affiliation1.中国科学院自动化研究所
2.大连理工大学
3.Dalian Maritime University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xiangzhu Meng,Lin Feng,Chonghui Guo,et al. A Unified Framework Based on Graph Consensus Term for Multiview Learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2022,35(3):3964 - 3977.
APA Xiangzhu Meng,Lin Feng,Chonghui Guo,Huibing Wang,&Shu Wu.(2022).A Unified Framework Based on Graph Consensus Term for Multiview Learning.IEEE Transactions on Neural Networks and Learning Systems,35(3),3964 - 3977.
MLA Xiangzhu Meng,et al."A Unified Framework Based on Graph Consensus Term for Multiview Learning".IEEE Transactions on Neural Networks and Learning Systems 35.3(2022):3964 - 3977.
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