Paper Info
Title
Multi-view Clustering with Graph Embedding for Connectome Analysis.
Abstract
Multi-view clustering has become a widely studied problem in the area of unsupervised learning. It aims to integrate multiple views by taking advantages of the consensus and complimentary information from multiple views. Most of the existing works in multi-view clustering utilize the vector-based representation for features in each view. However, in many real-world applications, instances are represented by graphs, where those vector-based models cannot fully capture the structure of the graphs from each view. To solve this problem, in this paper we propose a Multi-view Clustering framework on graph instances with Graph Embedding (MCGE). Specifically, we model the multi-view graph data as tensors and apply tensor factorization to learn the multi-view graph embeddings, thereby capturing the local structure of graphs. We build an iterative framework by incorporating multi-view graph embedding into the multi-view clustering task on graph instances, jointly performing multi-view clustering and multi-view graph embedding simultaneously. The multi-view clustering results are used for refining the multi-view graph embedding, and the updated multi-view graph embedding results further improve the multi-view clustering. Extensive experiments on two real brain network datasets (i.e., HIV and Bipolar) demonstrate the superior performance of the proposed MCGE approach in multi-view connectome analysis for clinical investigation and application.
Year
DOI
Venue
2017
10.1145/3132847.3132909
CIKM
Keywords
Field
DocType
Multi-view Clustering, Graph Embedding, Connectome Analysis
Data mining,Graph,Tensor,Graph embedding,Computer science,Connectome,Local structure,Unsupervised learning,Tensor factorization,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-1-4503-4918-5
11
0.50
References 
Authors
28
7
Name
Order
Citations
PageRank
Guixiang Ma1394.74
LiFang He2796.42
Chun-Ta Lu318315.10
Weixiang Shao41367.28
Philip S. Yu5306703474.16
Alex D. Leow651744.28
Ann B Ragin71347.69