Issue No. 10 - October (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.169
Yanhua Chen , Wayne State University, Detroit
Lijun Wang , Wayne State University, Detroit
Ming Dong , Wayne State University, Detroit
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, and image data sets, we demonstrate the superior performance of SS-NMF for heterogeneous data coclustering.
Non-negative matrix factorization, semisupervised clustering, heterogeneous data coclustering.
M. Dong, L. Wang and Y. Chen, "Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1459-1474, 2009.