Kernel-Based Restricted Boltzmann Machine for Unsupervised Feature Learning 


Vol. 44,  No. 9, pp. 1633-1640, Sep.  2019
10.7840/kics.2019.44.9.1633


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  Abstract

This paper presents a restricted Boltzmann machine (RBM) based on kernel method for unsupervised feature learning. The key idea of kernel RBM is that the input data is first implicitly mapped into a high-dimensional feature space, and the energy function is defined with the visible units and hidden units in that space. We propose the use of rectified linear unit for the kernel RBM as a kernel function, which is widely used in deep learning. The gradient-based contrastive divergence algorithm is used for the training of the kernel RBM, and the parameter update rules are derived for the learning. Experimental results on MNIST and STL-10 dataset show that the proposed approach can learn the useful representations, and it outperforms the conventional RBMs on the classification task.

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  Cite this article

[IEEE Style]

D. K. Kim and J. W. Shin, "Kernel-Based Restricted Boltzmann Machine for Unsupervised Feature Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 9, pp. 1633-1640, 2019. DOI: 10.7840/kics.2019.44.9.1633.

[ACM Style]

Dong Kook Kim and Jong Won Shin. 2019. Kernel-Based Restricted Boltzmann Machine for Unsupervised Feature Learning. The Journal of Korean Institute of Communications and Information Sciences, 44, 9, (2019), 1633-1640. DOI: 10.7840/kics.2019.44.9.1633.

[KICS Style]

Dong Kook Kim and Jong Won Shin, "Kernel-Based Restricted Boltzmann Machine for Unsupervised Feature Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 9, pp. 1633-1640, 9. 2019. (https://doi.org/10.7840/kics.2019.44.9.1633)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880