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Chris Ding, Tao Li, Michael I. Jordan, "Convex and SemiNonnegative Matrix Factorizations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 4555, January, 2010.  
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@article{ 10.1109/TPAMI.2008.277, author = {Chris Ding and Tao Li and Michael I. Jordan}, title = {Convex and SemiNonnegative Matrix Factorizations}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {32}, number = {1}, issn = {01628828}, year = {2010}, pages = {4555}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.277}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Convex and SemiNonnegative Matrix Factorizations IS  1 SN  01628828 SP45 EP55 EPD  4555 A1  Chris Ding, A1  Tao Li, A1  Michael I. Jordan, PY  2010 KW  Nonnegative matrix factorization KW  singular value decomposition KW  clustering. VL  32 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
[1] D. Lee and H.S. Seung, “Learning the Parts of Objects by NonNegative Matrix Factorization,” Nature, vol. 401, pp. 788791, 1999.
[2] D. Lee and H.S. Seung, “Algorithms for NonNegative Matrix Factorization,” Advances in Neural Information Processing Systems 13, MIT Press, 2001.
[3] P. Paatero and U. Tapper, “Positive Matrix Factorization: A NonNegative Factor Model with Optimal Utilization of Error Estimates of Data Values,” Environmetrics, vol. 5, pp. 111126, 1994.
[4] Y.L. Xie, P. Hopke, and P. Paatero, “Positive Matrix Factorization Applied to a Curve Resolution Problem,” J. Chemometrics, vol. 12, no. 6, pp. 357364, 1999.
[5] S. Li, X. Hou, H. Zhang, and Q. Cheng, “Learning Spatially Localized, PartsBased Representation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 207212, 2001.
[6] M. Cooper and J. Foote, “Summarizing Video Using NonNegative Similarity Matrix Factorization,” Proc. IEEE Workshop Multimedia Signal Processing, pp. 2528, 2002.
[7] W. Xu, X. Liu, and Y. Gong, “Document Clustering Based on NonNegative Matrix Factorization,” Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), pp. 267273, 2003.
[8] V.P. Pauca, F. Shahnaz, M. Berry, and R. Plemmons, “Text Mining Using NonNegative Matrix Factorization,” Proc. SIAM Int'l Conf. Data Mining, pp. 452456, 2004.
[9] J.P. Brunet, P. Tamayo, T. Golub, and J. Mesirov, “Metagenes and Molecular Pattern Discovery Using Matrix Factorization,” Proc. Nat'l Academy of Sciences USA, vol. 102, no. 12, pp. 41644169, 2004.
[10] H. Kim and H. Park, “Sparse NonNegative Matrix Factorizations via Alternating NonNegativityConstrained Least Squares for Microarray Data Analysis,” Bioinformatics, vol. 23, no. 12, pp. 14951502, 2007.
[11] D. Greene, G. Cagney, N. Krogan, and P. Cunningham, “Ensemble NonNegative Matrix Factorization Methods for Clustering ProteinProtein Interactions,” Bioinformatics, vol. 24, no. 15, pp.17221728, 2008.
[12] I. Dhillon and S. Sra, “Generalized Nonnegative Matrix Approximations with Bregman Divergences,” Advances in Neural Information Processing Systems 17, MIT Press, 2005.
[13] C. Ding, T. Li, and W. Peng, “Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing: Equivalence, ChiSquare Statistic, and a Hybrid Method,” Proc. Nat'l Conf. Artificial Intelligence, 2006.
[14] F. Sha, L.K. Saul, and D.D. Lee, “Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines,” Advances in Neural Information Processing Systems 15, MIT Press, 2003.
[15] N. Srebro, J. Rennie, and T. Jaakkola, “Maximum Margin Matrix Factorization,” Advances in Neural Information Processing Systems, MIT Press, 2005.
[16] P.O. Hoyer, “NonNegative Matrix Factorization with Sparseness Constraints,” J. Machine Learning Research, vol. 5, pp. 14571469, 2004.
[17] M. Berry, M. Browne, A. Langville, P. Pauca, and R. Plemmons, “Algorithms and Applications for Approximate Nonnegative Matrix Factorization,” Computational Statistics and Data Analysis, 2006.
[18] T. Li and S. Ma, “IFD: Iterative Feature and Data Clustering,” Proc. SIAM Int'l Conf. Data Mining, pp. 472476, 2004.
[19] T. Li, “A General Model for Clustering Binary Data,” Proc. Knowledge Discovery and Data Mining, pp. 188197, 2005.
[20] C. Ding, X. He, and H. Simon, “On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering,” Proc. SIAM Data Mining Conf., 2005.
[21] E. Gaussier and C. Goutte, “Relation between PLSA and NMF and Implications,” Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), pp. 601602, 2005.
[22] T. Hofmann, “Probabilistic Latent Semantic Indexing,” Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), pp.5057, 1999.
[23] D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet Allocation,” J.Machine Learning Research, vol. 3, pp. 9931022, 2003.
[24] M. Girolami and K. Kaban, “On an Equivalence between PLSI and LDA,” Proc. ACM Conf. Research and Development in Informational Retrieval (SIGIR), 2003.
[25] D. Lee and H.S. Seung, “Unsupervised Learning by Convex and Conic Coding,” Advances in Neural Information Processing Systems 9, MIT Press, 1997.
[26] L. Xu and M. Jordan, “On Convergence Properties of the EM Algorithm for Gaussian Mixtures,” Neural Computation, vol. 18, pp. 129151, 1996.
[27] C. Boutsidis and E. Gallopoulos, “SVD Based Initialization: A Head Start for Nonnegative Matrix Factorization,” Pattern Recognition, vol. 41, no. 4, pp. 13501362, 2008.
[28] D. Donoho and V. Stodden, “When Does NonNegative Matrix Factorization Give a Correct Decomposition into Parts?” Advances in Neural Information Processing Systems 16, MIT Press, 2004.
[29] A. D'Aspremont, L.E. Ghaoui, M.I. Jordan, and G.R.G. Lanckriet, “A Direct Formulation for Sparse PCA Using Semidefinite Programming,” SIAM Rev., 2006.
[30] H. Zou, T. Hastie, and R. Tibshirani, “Sparse Principal Component Analysis,” J. Computational and Graphical Statistics, vol. 15, pp. 265286, 2006.
[31] Z. Zhang, H. Zha, and H. Simon, “LowRank Approximations with Sparse Factors II: Penalized Methods with Discrete NewtonLike Iterations,” SIAM J. Matrix Analysis Applications, vol. 25, pp.901920, 2004.
[32] H. Zha, C. Ding, M. Gu, X. He, and H. Simon, “Spectral Relaxation for KMeans Clustering,” Advances in Neural Information Processing Systems 14, pp. 10571064, 2002.
[33] C. Ding and X. He, “KMeans Clustering and Principal Component Analysis,” Proc. Int'l Conf. Machine Learning, 2004.