Dictionary Learning Algorithm based on Restricted Boltzmann Machine

Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM.

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A Continuous Restricted Boltzmann Machine with a Hardware- Amenable Learning Algorithm

Artificial Neural Networks — ICANN 2002 - Lecture Notes in Computer Science ◽

10.1007/3-540-46084-5_58 ◽

2002 ◽

pp. 358-363 ◽

Cited By ~ 12

Author(s):

Hsin Chen ◽

Alan Murray

Keyword(s):

Learning Algorithm ◽

Restricted Boltzmann Machine ◽

Boltzmann Machine

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MapReduce based distributed learning algorithm for Restricted Boltzmann Machine

Neurocomputing ◽

10.1016/j.neucom.2015.09.129 ◽

2016 ◽

Vol 198 ◽

pp. 4-11 ◽

Cited By ~ 19

Author(s):

Chun-Yang Zhang ◽

C.L. Philip Chen ◽

Dewang Chen ◽

Kin Tek NG

Keyword(s):

Learning Algorithm ◽

Distributed Learning ◽

Restricted Boltzmann Machine ◽

Boltzmann Machine

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Spectral Classification Using Restricted Boltzmann Machine

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2013.38 ◽

2014 ◽

Vol 31 ◽

Cited By ~ 2

Author(s):

Chen Fuqiang ◽

Wu Yan ◽

Bu Yude ◽

Zhao Guodong

Keyword(s):

Graphical Model ◽

Learning Algorithm ◽

State Of The Art ◽

Cataclysmic Variables ◽

Original Data ◽

Restricted Boltzmann Machine ◽

Boltzmann Machine ◽

Spectral Classification ◽

Hidden Layer ◽

The Given

AbstractIn this study, a novel machine learning algorithm, restricted Boltzmann machine, is introduced. The algorithm is applied for the spectral classification in astronomy. Restricted Boltzmann machine is a bipartite generative graphical model with two separate layers (one visible layer and one hidden layer), which can extract higher level features to represent the original data. Despite generative, restricted Boltzmann machine can be used for classification when modified with a free energy and a soft-max function. Before spectral classification, the original data are binarised according to some rule. Then, we resort to the binary restricted Boltzmann machine to classify cataclysmic variables and non-cataclysmic variables (one half of all the given data for training and the other half for testing). The experiment result shows state-of-the-art accuracy of 100%, which indicates the efficiency of the binary restricted Boltzmann machine algorithm.

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