Improved Gaussian-Bernoulli Restricted Boltzmann Machine for Learning Discriminative Representations

• Impact Factor:8.139
• DOI number:10.1016/j.knosys.2019.104911
• Affiliation of Author(s):西南交通大学
• Journal:KNOWLEDGE-BASED SYSTEMS
• Place of Publication:NETHERLANDS
• Key Words:Representation learningGaussian–Bernoulli Restricted Boltzmann Machine (GRBM)Affinity matrixCD learning
• Abstract:Restricted Boltzmann machines (RBMs) have received considerable research interest in recent years because of their capability to discover latent representations in an unsupervised manner. The standard RBM is only suitable for processing binary-valued data. To address this limitation, the Gaussian–Bernoulli RBM (GRBM) has been designed to model real-valued data, particularly images. A GRBM that seeks to map real-valued data nonlinearly into a latent representation space is typically trained by contrastive divergence learning. However, most existing GRBM-based models neglect the inherent interpoint affinity information from the original data that can be used to enhance the expression ability of the model. In this study, a novel interpoint-affinity-based GRBM (abGRBM) is proposed to learn discriminative representations in the hidden layer. By incorporating the interpoint affinity information into the training process of the GRBM, the proposed model can not only utilize the GRBM’s powerful latent representation learning capabilities for real-valued data, it can also transform the original data into another space with improved separability. We prove the availability of our model using several image datasets of Microsoft Research Asia Multimedia for unsupervised clustering and supervised classification tasks. The experimental results show the superior performance of the GRBM in discovering discriminative representations and demonstrate the effectiveness of the affinity information.
• Co-author:Jielei Chu, Shudong Huang,Tianrui Li, Qigang Zhao
• First Author:Ji Zhang
• Indexed by:Academic papers
• Correspondence Author:Hongjun Wang
• Document Code:20193207289713
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 185
• Issue:1 December 2019
• Page Number:104911
• ISSN No.:0950-7051
• Translation or Not:no
• Date of Publication:2019-08-05