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An EM algorithm for learning sparse and overcomplete representations.

M. Zhong, H. Tang, H. Chen, and Y. Tang. Neurocomputing, (2004)

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A new fixed-point algorithm for independent component analysis.Z. Shi, H. Tang, and Y. Tang. Neurocomputing, (2004)Neuroscience data and tool sharing - A legal and policy framework for neuroinformatics.P. Eckersley, G. Egan, E. Schutter, Y. Tang, M. Novak, V. Sebesta, L. Matthiessen, I. Jääskeläinen, U. Ruotsalainen, A. Herz and 21 other author(s). Neuroinformatics, 1 (2): 149-165 (2003)An EM algorithm for learning sparse and overcomplete representations.M. Zhong, H. Tang, H. Chen, and Y. Tang. Neurocomputing, (2004)Blind source separation of more sources than mixtures using generalized exponential mixture models.Z. Shi, H. Tang, W. Liu, and Y. Tang. Neurocomputing, (2004)Second Language Proficiency and Attentional Network: Evidence from the Multi-Source Interference Task (MSIT).H. Wang, W. Liu, H. Chen, Y. Wang, D. Sui, and Y. Tang. ICNC (6), page 109-115. IEEE Computer Society, (2008)Expectation-Maximization approaches to independent component analysis.M. Zhong, H. Tang, and Y. Tang. Neurocomputing, (2004)Diagnose the mild cognitive impairment by constructing Bayesian network with missing data.Y. Sun, Y. Tang, S. Ding, S. Lv, and Y. Cui. Expert Syst. Appl., 38 (1): 442-449 (2011)Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine.S. Lv, X. Wang, Y. Cui, J. Jin, Y. Sun, Y. Tang, Y. Bai, Y. Wang, and L. Zhou. Computer Methods and Programs in Biomedicine, 97 (1): 11-18 (2010)Survival Density Particle Swarm Optimization for Neural Network Training.H. Liu, B. Li, X. Wang, Y. Ji, and Y. Tang. ISNN (1), volume 3173 of Lecture Notes in Computer Science, page 332-337. Springer, (2004)Neuroinformatics Research in China- Current Status and Future Research Activities.G. Li, J. Zhang, F. Gu, L. Yin, Y. Tang, and X. Tang. ICNC (1), volume 3610 of Lecture Notes in Computer Science, page 1052-1056. Springer, (2005)

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An EM algorithm for learning sparse and overcomplete representations.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML An EM algorithm for learning sparse and overcomplete representations.

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An EM algorithm for learning sparse and overcomplete representations.