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Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
October 2010 (vol. 22 no. 10)
pp. 1459-1474
ASCII Text
x 
 
Yanhua Chen, Lijun Wang, Ming Dong, "Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1459-1474, October, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2009.169,
author = {Yanhua Chen and Lijun Wang and Ming Dong},
title = {Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {22},
number = {10},
issn = {1041-4347},
year = {2010},
pages = {1459-1474},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.169},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering
IS - 10
SN - 1041-4347
SP1459
EP1474
EPD - 1459-1474
A1 - Yanhua Chen,
A1 - Lijun Wang,
A1 - Ming Dong,
PY - 2010
KW - Non-negative matrix factorization
KW - semisupervised clustering
KW - heterogeneous data coclustering.
VL - 22
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
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.

[1] E.M. Airoldi, D.M. Blei, S.E. Fienberg, and E.P. Xing, "Mixed Membership Stochastic Blockmodels," J. Machine Learning Research, vol. 9, pp. 1981-2014, 2008.
[2] L. Baker and A. McCallum, "Distributional Clustering of Words for Text Classification," Proc. 21st Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 96-103, 1998.
[3] A. Banerjee, I.S. Dhillon, J. Ghosh, S. Merugu, and D.S. Modha, "A Generalized Maximum Entropy Approach to Bregman Co-Clustering and Matrix Approximation," Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 509-514, 2004.
[4] R. Bekkerman and J. Jeon, "Multi-Modal Clustering for Multimedia Collections," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[5] R. Bekkerman and M. Sahami, "Semi-Supervised Clustering Using Combinatorial MRFs," Proc. 23rd Int'l Conf. Machine Learning (ICML) Workshop Learning in Structured Output Spaces, 2006.
[6] A. Blum and T. Mitchell, "Combining Labeled and Unlabeled Data with Co-Training," Proc. 11th Ann. Conf. Computational Learning Theory, pp. 92-100, 1998.
[7] D. Cai, Z. Shao, X. He, X. Yan, and J. Han, "Mining Hidden Community in Heterogeneous Social Networks," Proc. Workshop Link Discovery: Issues, Approaches and Applications, pp. 58-65, 2005.
[8] Y. Chen, M. Dong, and W. Wang, "Image Co-Clustering with Multi-Modality Features from User Feedbacks," Proc. ACM Int'l Conf. Multimedia, 2009.
[9] Y. Chen, M. Rege, M. Dong, and J. Hua, "Incorporating User Provided Constraints into Document Clustering," Proc. Seventh IEEE Int'l Conf. Data Mining, pp. 103-112, 2007.
[10] Y. Chen, L. Wang, and M. Dong, "A Matrix-Based Approach for Semi-Supervised Document Co-Clustering," Proc. 17th ACM Conf. Information and Knowledge Management, pp. 1523-1524, 2008.
[11] Y. Chen, M. Rege, M. Dong, and J. Hua, "Non-Negative Matrix Factorization for Semi-Supervised Data Clustering," J. Knowledge and Information Systems, vol. 17, pp. 355-379, 2008.
[12] F.R.K. Chung, Spectral Graph Theory. Am. Math. Soc., 1997.
[13] I.S. Dhillon, "Co-Clustering Documents and Words Using Bipartite Spectral Graph Partitioning," Proc. Seventh ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 269-274, 2001.
[14] I.S. Dhillon, S. Mallela, and D.S. Modha, "Information-Theoretic Co-Clustering," Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 89-98, 2003.
[15] C. Ding, "Unsupervised Feature Selection via Two-Way Ordering in Gene Expression Analysis," Bioinformatics, vol. 19, no. 10, pp. 1259-1266, 2003.
[16] C. Ding, T. Li, and W. Peng, "On the Equivalence between Non-Negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics and Data Analysis, vol. 52, no. 8, pp. 3913-3927, 2008.
[17] C. Ding, T. Li, W. Peng, and H. Park, "Orthogonal Nonnegative Matrix Tri-Factorizations for Clustering," Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 126-135, 2006.
[18] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. John Wiley & Sons, Inc., 2001.
[19] B. Gao, T.-Y. Liu, and W.-Y. Mao, "Star-Structured High-Order Heterogenous Data Co-Clustering Based on Consistent Information Theory," Proc. Sixth IEEE Int'l Conf. Data Mining, pp. 880-884, 2006.
[20] B. Gao, T.-Y. Liu, G. Feng, T. Qin, Q.-S. Cheng, and W.-Y. Ma, "Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Copartitioning," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 9, pp. 1263-1273, Sept. 2005.
[21] Z. Ghahramani and M.I. Jordan, "Supervised Learning from Incomplete Data via the Em Approach," Proc. Advances in Neural Information Processing Systems, pp. 120-127, 1994.
[22] E.-H. Han and G. Karypis, "Centroid-Based Document Classification: Analysis and Experimental Results," Proc. Fourth European Conf. Principles of Knowledge Discovery, pp. 424-431, 2000.
[23] M. Hiu, C. Law, A. Topchy, and A. Jain, "Model-Based Clustering with Probabilistic Constraints," Proc. Fifth SIAM Int'l Conf. Data Mining, pp. 641-645, 2005.
[24] T. Hoffman and J. Puzicha, "Latent Class Models for Collaborative Filtering," Proc. 16th Int'l Joint Conf. Artificial Intelligence, pp. 688-693, 1999.
[25] A.K. Jain, M.N. Murty, and P.J. Flynn, "Data Clustering: A Review," ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
[26] X. Ji and W. Xu, "Document Clustering with Prior Knowledge," Proc. 29th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 405-412, 2006.
[27] T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines," Proc. 16th Int'l Conf. Machine Learning, pp. 200-209, 1999.
[28] C. Kemp, J.B. Tenenbaum, T.L. Griffiths, T. Yamada, and N. Ueda, "Learning Systems of Concepts with an Infinite Relational Model," Proc. 21st Nat'l Conf. Artificial Intelligence, 2006.
[29] B. Kulis, S. Basu, I. Dhillon, and R. Mooney, "Semi-Supervised Graph Clustering: A Kernel Approach," Proc. 22nd Int'l Conf. Machine Learning, pp. 457-464, 2005.
[30] K. Lang, "Newsweeder: Learning to Filter Netnews," Proc. 12th Int'l Conf. Machine Learning, pp. 331-339, 1995.
[31] D. Lee and H. Seung, "Learning the Parts of Objects by Non-Negative Matrix Factorization," Nature, vol. 401, pp. 788-791, 1999.
[32] D. Lee and H. Seung, "Algorithms for Non-Negative Matrix Factorization," Proc. 13th Advances in Neural Information Processing Systems, pp. 556-562, 2001.
[33] X. Liu, Y. Gong, W. Xu, and S. Zhu, "Document Clustering with Cluster Refinement and Model Selection Capabilities," Proc. 25th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 191-198, 2002.
[34] B. Long, X. Wu, Z. Zhang, and P.S. Yu, "Spectral Clustering for Multi-Type Relational Data," Proc. 23rd Int'l Conf. Machine Learning, pp. 585-592, 2006.
[35] B. Long, Z. Zhang, and P.S. Yu, "A Probabilistic Framework for Relational Clustering," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 470-479, 2007.
[36] W.-Y. Max and H. Zhang, "Benchmarking of Image Features for Content-Based Retrieval," Proc. 32nd Asilomar Conf. Signals, Systems and Computers, pp. 253-257, 1998.
[37] H. Shan and A. Banerjee, "Bayesian Co-Clustering," Proc. 13th IEEE Int'l Conf. Data Mining, pp. 530-539, 2008.
[38] A. Vailaya, A. Jain, and H. Zhang, "On Image Classification: City Images vs. Landscapes," Pattern Recognition, vol. 31, no. 12, pp. 1921-1935, 1998.
[39] P. Willett, "Recent Trends in Hierarchic Document Clustering: A Critical Review," Int'l J. Information Processing and Management, vol. 24, no. 5, pp. 577-597, 1988.
[40] E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell, "Distance Metric Learning, with Application to Clustering with Side Information," Proc. 16th Neural Information Processing Systems, pp. 505-512, 2002.
[41] W. Xu, X. Liu, and Y. Gong, "Document Clustering Based on Non-Negative Matrix Factorization," Proc. 26th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 267-273, 2003.

Index Terms:
Non-negative matrix factorization, semisupervised clustering, heterogeneous data coclustering.
Citation:
Yanhua Chen, Lijun Wang, Ming Dong, "Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1459-1474, Oct. 2010, doi:10.1109/TKDE.2009.169
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