论文信息 - An EM algorithm for learning sparse and overcomplete representations

An EM algorithm for learning sparse and overcomplete representations

An expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments,enable the development of an EM algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients. (C) 2003 Elsevier B.V. All rights reserved.

[1] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[3] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .

[4] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[5] Aapo Hyvärinen,et al. Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood , 1998, Neurocomputing.

[6] Mark A. Girolami,et al. A Variational Method for Learning Sparse and Overcomplete Representations , 2001, Neural Computation.

[7] Terrence J. Sejnowski,et al. Learning Overcomplete Representations , 2000, Neural Computation.

[8] E. Oja,et al. Independent Component Analysis , 2013 .

[9] Bruno A. Olshausen,et al. PROBABILISTIC FRAMEWORK FOR THE ADAPTATION AND COMPARISON OF IMAGE CODES , 1999 .

[10] Terrence J. Sejnowski,et al. Blind source separation of more sources than mixtures using overcomplete representations , 1999, IEEE Signal Processing Letters.

[11] Barak A. Pearlmutter,et al. Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[12] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.