硕士生导师
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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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Restricted Boltzmann machines with Gaussian visible units guided by pairwise constraints
影响因子：19.118
DOI码：10.1109/TCYB.2018.2863601
所属单位：西南交通大学
发表刊物：IEEE Transactions on Cybernetics
刊物所在地：UNITED STATES
关键字：Contrastive divergence (CD), pairwise constraints (PCs), restricted Boltzmann machine (RBM), semi-supervised clustering, unsupervised clustering
摘要：Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach, without any guidances of the background knowledge. To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints (PCs) RBM with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by PCs and the process of encoding is conducted under these guidances. The PCs are encoded in hidden layer features of pcGRBM. Then, some pairwise hidden features of pcGRBM flock together and another part of them are separated by the guidances. In order to deal with real-valued data, the binary visible units are replaced by linear units with Gaussian noise in the pcGRBM model. In the learning process of pcGRBM, the PCs are iterated transitions between visible and hidden units during CD learning procedure. Then, the proposed model is inferred by approximative gradient descent method and the corresponding learning algorithm is designed. In order to compare the availability of pcGRBM and traditional RBMs with Gaussian visible units, the features of the pcGRBM and RBMs hidden layer are used as input “data” for K-means, spectral clustering (SP) and affinity propagation (AP) algorithms, respectively. We also use tenfold cross-validation strategy to train and test pcGRBM model to obtain more meaningful results with PCs which are derived from incremental sampling procedures. A thorough experimental evaluation is performed with 12 image datasets of Microsoft Research Asia Multimedia. The experimental results show that the clustering performance of K-means, SP, and AP algorithms based on pcGRBM model are significantly better than traditional RBMs. In addition, the pcGRBM model for clustering tasks shows better performance than some semi-supervised clustering algorithms.
合写作者：Hua Meng, Peng Jin,李天瑞
第一作者：Jielei Chu
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20183605782787
学科门类：工学
一级学科：计算机科学与技术
页面范围：4321 - 4334
ISSN号：2168-2275
是否译文：否
发表时间：2018-08-23