硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Improved Gaussian-Bernoulli Restricted Boltzmann Machine for Learning Discriminative Representations
影响因子：8.139
DOI码：10.1016/j.knosys.2019.104911
所属单位：西南交通大学
发表刊物：KNOWLEDGE-BASED SYSTEMS
刊物所在地：NETHERLANDS
关键字：Representation learningGaussian–Bernoulli Restricted Boltzmann Machine (GRBM)Affinity matrixCD learning
摘要：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.
合写作者：Jielei Chu, Shudong Huang,李天瑞, Qigang Zhao
第一作者：Ji Zhang
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20193207289713
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 185
期号：1 December 2019
页面范围：104911
ISSN号：0950-7051
是否译文：否
发表时间：2019-08-05