.png){kind=icon}
返回
A Unified Framework Based on Graph Consensus Term for Multiview Learning
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2022.3201498.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Learning systems
Correlation
Task analysis
Kernel
Iterative methods
Proposals
Optimization
Graph consensus term
iterative alternating strategy
multiview learning
unified framework
期刊
IF:
8.9
论文数:
7.5K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
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The roles of actin cytoskeleton and microtubules for membrane recycling of a food vacuole in Tetrahymena thermophila 嗜热四膜虫 中肌动蛋白细胞骨架和微管在食品液泡膜循环中的作用
PREAI
分享收藏
查看更多内容