Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine

Dongdong Chen, Jiancheng Lv, Mike E. Davies

Research output: Contribution to conferencePosterpeer-review

Abstract / Description of output

We investigate the potential of a restricted Boltzmann Machine (RBM) for discriminative representation learning. By imposing the class information preservation constraints on the hidden layer of the RBM, we propose a Signed Laplacian Restricted Boltzmann Machine (SLRBM) for supervised discriminative representation learning. The model utilizes the label information and preserves the global data locality of data points simultaneously. Experimental results on the benchmark data set show the effectiveness of our method.
Original languageUndefined/Unknown
Publication statusPublished - 28 Aug 2018
EventiTWIST 2018 -
Duration: 19 Nov 201823 Nov 2018


ConferenceiTWIST 2018

Keywords / Materials (for Non-textual outputs)

  • cs.CV

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