Abstract
We extend the Gaussian scale mixture model of dependent subspace source densities to include non-radially symmetric densities using Generalized Gaussian random variables linked by a common variance. We also introduce the modeling of skew in source densities and subspaces using a generalization of the Normal Variance-Mean mixture model. We give closed form expressions for subspace likelihoods and parameter updates in the EM algorithm.
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Palmer, J.A., Kreutz-Delgado, K., Rao, B.D., Makeig, S. (2007). Modeling and Estimation of Dependent Subspaces with Non-radially Symmetric and Skewed Densities. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_13
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DOI: https://doi.org/10.1007/978-3-540-74494-8_13
Publisher Name: Springer, Berlin, Heidelberg
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