标题:Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition
作者:Yuan, Longhao ;Cao, Jianting ;Zhao, Xuyang ;Wu, Qiang ;Zhao, Qibin
通讯作者:Cao, Jianting
作者机构:[Yuan, Longhao ;Cao, Jianting ;Zhao, Xuyang ] Graduate School of Engineering, Saitama Institute of Technology, Japan;[Zhao, Qibin ] Tensor Learning Un 更多
会议名称:10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
会议日期:12 November 2018 through 15 November 2018
来源:2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
出版年:2019
页码:1071-1076
DOI:10.23919/APSIPA.2018.8659708
摘要:The problem of incomplete data is common in signal processing and machine learning. Tensor completion algorithms aim to recover the incomplete data from its partially observed entries. In this paper, taking advantages of high compressibility and flexibility of recently proposed tensor ring (TR) decomposition, we propose a new tensor completion approach named tensor ring weighted optimization (TR-WOPT). It finds the latent factors of the incomplete tensor by gradient descent algorithm, then the latent factors are employed to predict the missing entries of the tensor. We conduct various tensor completion experiments on synthetic data and real-world data. The simulation results show that TR-WOPT performs well in various high-dimension tensors. Furthermore, image completion results show that our proposed algorithm outperforms the state-of-the-art algorithms in many situations. Especially when the missing rate of the test images is high (e.g., over 0.9), the performance of our TR-WOPT is significantly better than the compared algorithms. © 2018 APSIPA organization.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063545860&doi=10.23919%2fAPSIPA.2018.8659708&partnerID=40&md5=3ba3f75c389ed6eeb9bd086ab8b39394
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