NDLI: Improving the Generalization Ability of Restricted Boltzmann Machines via Theta Pure Dependency

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Improving the Generalization Ability of Restricted Boltzmann Machines via Theta Pure Dependency

Content Provider	ACM Digital Library
Author	Hou, Yuexian Xu, Qi
Abstract	The Restricted Boltzmann Machine (RBM) is an important probabilistic graphical model which often serves as a building block for deep belief networks (DBNs). A critical issue for DBNs is overfitting. The generalization ability of models is affected if an inappropriate model complexity is selected. In order to mitigate this problem, we introduce the Pure Dependency-Restricted Boltzmann Machine (PD-RBM). Compared to the regular RBM, the PD-RBM can adaptively change its structure in the face of different data in order to make its model complexity fit data. The establishment of the structure of PD-RBMs is based on Theta Pure Dependency (TPD), which is a statistical measure of variable dependency, defined under the Information Geometry framework. When specific data is given, we show an algorithm that is used to establish the structure of PD-RBMs. We evaluate the PD-RBM on different datasets. Our experiments show that it significantly improves test performance.
Starting Page	41
Ending Page	45
Page Count	5
File Format	PDF
ISBN	9781450352321
DOI	10.1145/3094243.3094246
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rbm Generalization ability Overfitting
Content Type	Text
Resource Type	Article

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