Representational Transfer Learning for Matrix Completion
Guardado en:
| 发表在: | arXiv.org (Dec 9, 2024), p. n/a |
|---|---|
| 主要作者: | |
| 其他作者: | , , , |
| 出版: |
Cornell University Library, arXiv.org
|
| 主题: | |
| 在线阅读: | Citation/Abstract Full text outside of ProQuest |
| 标签: |
没有标签, 成为第一个标记此记录!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3142734131 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3142734131 | ||
| 045 | 0 | |b d20241209 | |
| 100 | 1 | |a He, Yong | |
| 245 | 1 | |a Representational Transfer Learning for Matrix Completion | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 9, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a We propose to transfer representational knowledge from multiple sources to a target noisy matrix completion task by aggregating singular subspaces information. Under our representational similarity framework, we first integrate linear representation information by solving a two-way principal component analysis problem based on a properly debiased matrix-valued dataset. After acquiring better column and row representation estimators from the sources, the original high-dimensional target matrix completion problem is then transformed into a low-dimensional linear regression, of which the statistical efficiency is guaranteed. A variety of extensional arguments, including post-transfer statistical inference and robustness against negative transfer, are also discussed alongside. Finally, extensive simulation results and a number of real data cases are reported to support our claims. | |
| 653 | |a Knowledge management | ||
| 653 | |a Matrices (mathematics) | ||
| 653 | |a Target acquisition | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Subspaces | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Statistical inference | ||
| 653 | |a Knowledge representation | ||
| 700 | 1 | |a Li, Zeyu | |
| 700 | 1 | |a Liu, Dong | |
| 700 | 1 | |a Qin, Kangxiang | |
| 700 | 1 | |a Xie, Jiahui | |
| 773 | 0 | |t arXiv.org |g (Dec 9, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3142734131/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.06233 |