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Optimize real-valued RBM with Bidirectional Autoencoder
Feng Q.; Chen L.; Chen C.L.P.
Conference NameInternational Conference on Fuzzy Theory and Its Applications (IFUZZY)
Source PublicationiFUZZY 2015 - 2015 International Conference on Fuzzy Theory and Its Applications, Conference Digest
Conference DateNOV 18-20, 2015
Conference PlaceYilan, TAIWAN

Deep learning increasingly attracted attention after the fast training method of Restricted Boltzmann Machine(RBM) isproposed[1]. Many researches directly constructed deep architecture with stack RBMs to learn the representation of the data, few studied the optimization method to get good RBM parameters. Here proposes a new optimization method for real-valued RBM by minimizing the reconstructed error. Firstly, build and initialize Bidirectional Autoencoder(Bi-Ae). Secondly, minimize the cost function with Stochastic Gradient Descent (SGD) to get the parameters. Thirdly, convert the Bi-AE into RBM with the most suitable parameters. Experiments are executed on the MNIST dataset. Compared with PSO and likelihood maximum optimization methods, the reconstructed errors of the proposed method is 4.02% smaller than the result from error[1], which is advanced.

KeywordBack Propagation Bidirectional Autoencoder Rbm Optimization Sgd
URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000380618900005
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Feng Q.,Chen L.,Chen C.L.P.. Optimize real-valued RBM with Bidirectional Autoencoder[C],2016:22-27.
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