TY - GEN
T1 - Multi-view clustering with graph embedding for connectome analysis
AU - Ma, Guixiang
AU - He, Lifang
AU - Lu, Chun Ta
AU - Shao, Weixiang
AU - Yu, Philip S.
AU - Leow, Alex D.
AU - Ragin, Ann B.
N1 - Funding Information:
performing multi-view clustering and graph embedding simultaneously. The results of multi-view clustering are used to refine the multi-view graph embeddings, in turn, the updated multi-view graph embedding results are used to improve the multi-view clustering. By updating the clustering results and graph embeddings iteratively, the proposed MCGE framework will result in a better multi-view clustering solution. We apply our MCGE framework for unsupervised multi-view connectome analysis on HIV-induced brain alterations and bipolar affective disorder. Extensive experimental results on real multi-view HIV brain network data and Bipolar brain network data show the effectiveness of MCGE for multi-view clustering in connectome analysis. ACKNOWLEDGMENTS This work is supported in part by NSF through grants IIS-1526499, and CNS-1626432, and NSFC 61672313, and NSFC 61503253.
Funding Information:
This work is supported in part by NSF through grants IIS-1526499, and CNS-1626432, and NSFC 61672313, and NSFC 61503253.
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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.
AB - 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.
KW - Connectome Analysis
KW - Graph Embedding
KW - Multi-view Clustering
UR - http://www.scopus.com/inward/record.url?scp=85037361395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037361395&partnerID=8YFLogxK
U2 - 10.1145/3132847.3132909
DO - 10.1145/3132847.3132909
M3 - Conference contribution
AN - SCOPUS:85037361395
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 127
EP - 136
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -