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TKDE
2010

Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering

13 years 3 days ago
Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
Coclustering heterogeneous data has attracted extensive attention recently due to its high impact on various important applications, such us text mining, image retrieval, and bioinformatics. However, data coclustering without any prior knowledge or background information is still a challenging problem. In this paper, we propose a Semisupervised Non-negative Matrix Factorization (SS-NMF) framework for data coclustering. Specifically, our method computes new relational matrices by incorporating user provided constraints through simultaneous distance metric learning and modality selection. Using an iterative algorithm, we then perform trifactorizations of the new matrices to infer the clusters of different data types and their correspondence. Theoretically, we prove the convergence and correctness of SS-NMF coclustering and show the relationship between SS-NMF with other well-known coclustering models. Through extensive experiments conducted on publicly available text, gene expression, an...
Yanhua Chen, Lijun Wang, Ming Dong
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TKDE
Authors Yanhua Chen, Lijun Wang, Ming Dong
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