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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Multi-local Collaborative AutoEncoder
影响因子：8.139
DOI码：10.1016/j.knosys.2021.107844
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：Restricted Boltzmann machine;Autoencoder;Deep collaborative representation;Feature learning;Unsupervised clustering;
摘要：The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper presents a Multi-local Collaborative AutoEncoder (MC-AE), which consists of novel multi-local collaborative representation RBM (mcrRBM) and multi-local collaborative representation GRBM (mcrGRBM) models. Here, the Locality Sensitive Hashing (LSH) method is used to divide the input data into multi-local cross blocks which contains multi-local collaborative relationships of the unlabeled data and features since the similar multi-local instances and features of the input data are divided into the same block. In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure. Then, the local hidden features converges on the center of each local collaborative block. Under the collaborative joint influence of each local block, the proposed MC-AE has powerful capability of representation learning for unsupervised clustering. However, our MC-AE model perhaps perform training process for a long time on the large-scale and high-dimensional datasets because more local collaborative blocks are integrate into it. Five most related deep models are compared with our MC-AE. The experimental results show that the proposed MC-AE has more excellent capabilities of collaborative representation and generalization than the contrastive deep models.
合写作者：Jing Liu, Zeng Yu,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：20220211432876
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 239
期号：5 March 2022
页面范围：107844
ISSN号：0950-7051
是否译文：否
发表时间：2021-12-29