Orthogonal nonnegative matrix tri-factorization for semi-supervised document co-clustering

Authors:
Huifang Ma;Weizhong Zhao;Qing Tan;Zhongzhi Shi
Affiliations:
,Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;,Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;,Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;,Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2010

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Abstract

Semi-supervised clustering is often viewed as using labeled data to aid the clustering process However, existing algorithms fail to consider dual constraints between data points (e.g documents) and features (e.g words) To address this problem, in this paper, we propose a novel semi-supervised document co-clustering model OSS-NMF via orthogonal nonnegative matrix tri-factorization Our model incorporates prior knowledge both on document and word side to aid the new word-category and document-cluster matrices construction Besides, we prove the correctness and convergence of our model to demonstrate its mathematical rigorous Our experimental evaluations show that the proposed document clustering model presents remarkable performance improvements with certain constraints.