Share  Bookmark

 Download from


 Funded by

[1] X.Wu, X. Zhu, G.Q. Wu, W. Ding, “Data Mining with Big Data,” IEEE Trans. On Knowledge and Data Engineering, vol. 26, no. 1, pp. 97107, Jan. 2014.
[2] M.J. Wainwright, M.I. Jordan, “Graphical Models, Exponential Families, and Variational Inference,” Foundations and Trends in Machine Learning, vol. 1, no. 12, pp. 1305, January 2008.
[3] J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, 2006.
[4] U. Fayyad, G. PiatetskyShapiro, R. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, vol. 17, no. 3, pp. 3754, 1996.
[5] F. V. Jensen and T. D. Nielsen, Bayesian Networks and Decision Graphs, 2nd ed., Springer, 2007.
[6] D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques., The MIT Press, 2009.
[7] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, 2nd edition, Springer, 2009.
[8] R. Silva, R. Scheines, C. Glymour, P. Spirtes, “Learning the Structure of Linear Latent Variable Models,” J. of Machine Learning Research, vol. 7, pp. 191246, 2006.
[9] J.B. Tenenbaum, V. de Silva, J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, pp. 23192323, December 22, 2000.
[10] S.T. Roweis, L.K. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, pp. 23232326, December 22, 2000.