TY - GEN
T1 - Multi-view clustering via joint nonnegative matrix factorization
AU - Liu, Jialu
AU - Wang, Chi
AU - Gao, Jing
AU - Han, Jiawei
N1 - Publisher Copyright:
Copyright © SIAM.
PY - 2013
Y1 - 2013
N2 - Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In this paper, we propose a novel NMF-based multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views. The key idea is to formulate a joint matrix factorization process with the constraint that pushes clustering solution of each view towards a common consensus instead of fixing it directly. The main challenge is how to keep clustering solutions across different views meaningful and comparable. To tackle this challenge, we design a novel and effective normalization strategy inspired by the connection between NMF and PLSA. Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.
AB - Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In this paper, we propose a novel NMF-based multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views. The key idea is to formulate a joint matrix factorization process with the constraint that pushes clustering solution of each view towards a common consensus instead of fixing it directly. The main challenge is how to keep clustering solutions across different views meaningful and comparable. To tackle this challenge, we design a novel and effective normalization strategy inspired by the connection between NMF and PLSA. Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=84886433522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886433522&partnerID=8YFLogxK
U2 - 10.1137/1.9781611972832.28
DO - 10.1137/1.9781611972832.28
M3 - Conference contribution
AN - SCOPUS:84886433522
T3 - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
SP - 252
EP - 260
BT - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
A2 - Ghosh, Joydeep
A2 - Obradovic, Zoran
A2 - Dy, Jennifer
A2 - Zhou, Zhi-Hua
A2 - Kamath, Chandrika
A2 - Parthasarathy, Srinivasan
PB - Siam Society
T2 - SIAM International Conference on Data Mining, SDM 2013
Y2 - 2 May 2013 through 4 May 2013
ER -