@inproceedings{e4e9266488ea42e79cbc66533feea081,
title = "Multi-view clustering via joint nonnegative matrix factorization",
abstract = "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.",
author = "Jialu Liu and Chi Wang and Jing Gao and Jiawei Han",
year = "2013",
month = jan,
day = "1",
doi = "10.1137/1.9781611972832.28",
language = "English (US)",
series = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",
publisher = "Siam Society",
pages = "252--260",
editor = "Joydeep Ghosh and Zoran Obradovic and Jennifer Dy and Zhi-Hua Zhou and Chandrika Kamath and Srinivasan Parthasarathy",
booktitle = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",
note = "SIAM International Conference on Data Mining, SDM 2013 ; Conference date: 02-05-2013 Through 04-05-2013",
}