arrow
返回

Structured Optimal Graph-Based Clustering With Flexible Embedding

delete2020-10-01
delete11
PRE
AI
P
Pengzhen Ren
Y
Yun Xiao *
X
Xiaojun Chang *
M
Mahesh Prakash
聂飞平 (Feiping Nie)
X
Xin Wang
X
Xiaojiang Chen
DOI:10.1109/TNNLS.2019.2946329delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
In the real world, the duality of high-dimensional data is widespread. The coclustering method has been widely used because they can exploit the co-occurring structure between samples and features. In fact, most of the existing coclustering methods cluster the graphs in the original data matrix. However, these methods fail to output an affinity graph with an explicit cluster structure and still call for the postprocessing step to obtain the final clustering results. In addition, these methods are difficult to find a good projection direction to complete the clustering task on high-dimensional data. In this article, we modify the flexible manifold embedding theory and embed it into the bipartite spectral graph partition. Then, we propose a new method called structured optimal graph-based clustering with flexible embedding (SOGFE). The SOGFE method can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step. Additionally, the SOGFE method can learn a suitable projection direction to map high-dimensional data to a low-dimensional subspace. We perform extensive experiments on two synthetic data sets and seven benchmark data sets. The experimental results verify the superiority, robustness, and good projection direction selection ability of our proposed method.
Keyword:
Clustering algorithms
Bipartite graph
Laplace equations
Clustering methods
Mathematical model
Task analysis
Manifolds
Bipartite graph
coclustering
flexible embedding
unsupervised learning
AI总结

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

IEEE Transactions on Neural Networks and Learning Systems 封面图
IEEE Transactions on Neural Networks and Learning Systems
IF:
8.9
论文数:
7.5K
被引数:
7.2W

机构

U
University of Calgary
学者数:
3.8W
论文数: 3.3W
被引数: 52
M
Monash University
学者数:
5.4W
论文数: 5.4W
被引数: 79
N
Northwestern Polytechnical University
学者数:
4.6W
论文数: 3.7W
被引数: 5.3W
N
northwest university xi'an
学者数:
1.8W
论文数: 1.2W
被引数: 22
C
学者 查看更多机构
引用论文

引用论文

Environmental engagements through the lens of disclosure practices
err2006-03-01
err0
PREAI
errHaslinda Yusoff; Glen Lehman; Noraini Mohd Nasir
err分享
err收藏
err分享
err收藏
Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation
err2016-08-01
err105
PREAI
errFang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung
err分享
err收藏
Rank-Constrained Spectral Clustering With Flexible Embedding
err2018-12-01
err217
PREAI
errLi, Zhihui; Nie, Feiping; Chang, Xiaojun; Nie, Liqiang; Zhang, Huaxiang; Yang, Yi
err分享
err收藏
Gene clustering based on RNAi phenotypes of ovary-enriched genes in C-elegans
err2002-11-01
err242
errOAAI
errPiano, F; Schetter, AJ; Morton, DG; Gunsalus, KC; Reinke, V; Kim, SK; Kemphues, KJ
err分享
err收藏
学者 查看更多内容