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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Bilateral discriminative autoencoder model orienting co-representation learning
影响因子：8.139
DOI码：10.1016/j.knosys.2022.108653
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 learning,Autoencoder,Co-clustering,Self-supervised learning
摘要：Autoencoder is an important representation learning model which has attracted extensive research attention. However, an autoencoder learns latent representation by reducing reconstruction error without emphasis on discrimination, which is vital to downstream machine learning tasks like classification and clustering. Many existing works have improved the discrimination of autoencoders. But as far as we know, there is no work focusing on bilateral discriminative representation learning(i.e. co-representation learning). Our work unlocks the potential of autoencoder on co-representation learning and proposes a bilateral discriminative autoencoder model for co-representation learning(CRBDAE). By utilizing a fuzzy set, the topological relationship between samples and features is represented as fuzzy information. In the bilateral discriminative autoencoder, by means of regularization, fuzzy information is employed to enhance the self-supervised co-representation learning ability. Thus, the corresponding loss function is illustrated. We also inferred the parameters updating method and proposed the model training algorithm. Finally, the availability of the CRBDAE model was demonstrated on 12 datasets and the results proved that the performance of the proposed model meets our expectations.
合写作者：Wei Chen, Luqing Wang,李天瑞
第一作者：Zehao Liu
论文类型：学术论文
通讯作者：Hongjun Wang
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 245
期号：2022,108653,
页面范围：108653
ISSN号：0950-7051
是否译文：否
发表时间：2022-05-23