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231224s2013 xx |||||o 00| ||eng c |
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|a eng
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|a Chen, Bo
|e verfasserin
|4 aut
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| 245 |
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|a Deep learning with hierarchical convolutional factor analysis
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|c 2013
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|a Text
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|a ƒaComputermedien
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|a Date Completed 17.02.2014
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|a Date Revised 23.03.2024
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Unsupervised multilayered (“deep”) models are considered for imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis that explicitly exploit the convolutional nature of the expansion. To address large-scale and streaming data, an online version of VB is also developed. The number of dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature
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|a Journal Article
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|a Polatkan, Gungor
|e verfasserin
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| 700 |
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|a Sapiro, Guillermo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Blei, David
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dunson, David
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Carin, Lawrence
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 35(2013), 8 vom: 19. Aug., Seite 1887-901
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|x 1939-3539
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|u http://dx.doi.org/10.1109/TPAMI.2013.19
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