ShanghaiTech University Knowledge Management System
Tensor Train Random Projection | |
2023 | |
发表期刊 | CMES - COMPUTER MODELING IN ENGINEERING AND SCIENCES |
ISSN | 1526-1492 |
EISSN | 1526-1506 |
卷号 | 134期号:2页码:1197-1218 |
发表状态 | 已发表 |
DOI | 10.32604/cmes.2022.021636 |
摘要 | This work proposes a Tensor Train Random Projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically constructed through a Tensor Train (TT) representation with TT-ranks equal to one. Based on the tensor train format, this random projection method can speed up the dimension reduction procedure for high-dimensional datasets and requires fewer storage costs with little loss in accuracy, compared with existing methods. We provide a theoretical analysis of the bias and the variance of TTRP, which shows that this approach is an expected isometric projection with bounded variance, and we show that the scaling Rademacher variable is an optimal choice for generating the corresponding TT-cores. Detailed numerical experiments with synthetic datasets and the MNIST dataset are conducted to demonstrate the efficiency of TTRP. © 2023 Tech Science Press. All rights reserved. |
关键词 | Dimension reduction High-dimensional dataset Isometric projections Pairwise distances Random projection methods Random projections Scalings Speed up Storage costs Tensor trains |
URL | 查看原文 |
收录类别 | EI ; SCOPUS |
语种 | 英语 |
出版者 | Tech Science Press |
EI入藏号 | 20224012817970 |
EI主题词 | Tensors |
EI分类号 | 921.1 Algebra |
原始文献类型 | Journal article (JA) |
Scopus 记录号 | 2-s2.0-85138801675 |
来源库 | Scopus |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/235988 |
专题 | 信息科学与技术学院_PI研究组_周平强组 信息科学与技术学院_PI研究组_廖奇峰组 信息科学与技术学院_博士生 |
通讯作者 | Liao, Qifeng |
作者单位 | 1.School of Information Science and Technology,ShanghaiTech University,Shanghai,201210,China 2.Peng Cheng Laboratory,Shenzhen,518055,China 3.Innovation Academy for Microsatellites,Chinese Academy of Sciences,Shanghai,201210,China |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Feng, Yani,Tang, Kejun,He, Lianxing,et al. Tensor Train Random Projection[J]. CMES - COMPUTER MODELING IN ENGINEERING AND SCIENCES,2023,134(2):1197-1218. |
APA | Feng, Yani,Tang, Kejun,He, Lianxing,Zhou, Pingqiang,&Liao, Qifeng.(2023).Tensor Train Random Projection.CMES - COMPUTER MODELING IN ENGINEERING AND SCIENCES,134(2),1197-1218. |
MLA | Feng, Yani,et al."Tensor Train Random Projection".CMES - COMPUTER MODELING IN ENGINEERING AND SCIENCES 134.2(2023):1197-1218. |
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