NDLI: Maximum reconstruction probability training of Restricted Boltzmann machines with auxiliary function approach

Content Provider	IEEE Xplore Digital Library
Author	Takamune, N. Kameoka, H.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Inf. Sci. & Technol., Univ. of Tokyo, Tokyo, Japan (Takamune, N.; Kameoka, H.)
Abstract	Restricted Boltzmann machines (RBMs) are stochastic neural networks that can be used to learn features from raw data. They have attracted particular attention recently after being proposed as building blocks for deep belief network (DBN) and have been applied with notable success in a range of problems including speech recognition and object recognition. The success of these models raises the issue of how best to train them. At present, the most popular training algorithm for RBMs is the Contrastive Divergence (CD) learning algorithm. We propose deriving a new training algorithm based on an auxiliary function approach for RBMs using the reconstruction probability of observations as the optimization criterion. Through an experiment on parameter training of an RBM, we confirmed that the present algorithm outperformed the CD algorithm in terms of the convergence speed and the reconstruction error when used as an autoencoder.
Starting Page	1
Ending Page	6
File Size	182633
Page Count	6
File Format	PDF
ISBN	9781479936946
DOI	10.1109/MLSP.2014.6958881
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Optimization Error analysis Linear programming Signal processing algorithms Convergence Algorithm design and analysis auxiliary function approach Deep learning deep belief networks restricted Boltzmann machine
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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