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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Auto-attention Mechanism for Multi-view Deep Embedding Clustering
影响因子：8.4
DOI码：10.1016/j.patcog.2023.109764
所属单位：西南交通大学
发表刊物：Pattern Recognition
刊物所在地：ENGLAND
关键字：Deep embedding clustering; Deep multi-view clustering; Multi-view autoencoder; Auto-attention
摘要：In several fields, deep learning has achieved tremendous success. Multi-view learning is a workable method for handling data from several sources. For clustering multi-view data, deep learning and multi-view learning are excellent options. However, a persistent challenge is a need for the current deep learning approach to independently drive divergent neural networks for different perspectives while working with multi-view data. The current methods use the number of viewpoints to calculate neural network statistics. Consequently, as the number of views rises, it results in a considerable calculation. Furthermore, they vainly try to unite various viewpoints at the training. Incorporating a triple fusion technique, this research suggests an innovative multi-view deep embedding clustering (MDEC) model. The suggested model can jointly acquire the specific knowledge in each view as well as the information fragment of the collective views. The main goal of the MDEC is to lower the errors made when learning the features of each view and correlating data from many views. To address the optimization problem, the MDEC model advises a suitable iterative updating approach. In testing modern deep learning and non-deep learning algorithms, the experimental study on small and large-scale multi-view data shows encouraging results for the MDEC model. In multi-view clustering, this work demonstrates the benefit of the deep learning-based approach over the non-ones. However, future work will address a variety of issues related to MDEC including the speed.
合写作者：Tianrui Li,Ghufran Ahmad Khan,Xinyan Liang,Hongjun Wang
第一作者：Bassoma Diallo
论文类型：SCI
通讯作者：Jie Hu
学科门类：工学
文献类型：J
卷号：143
页面范围：109764
ISSN号：0031-3203
是否译文：否
发表时间：2023-06-17
收录刊物：SCI