Representational Transfer Learning for Matrix Completion

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Dettagli Bibliografici
Pubblicato in:	arXiv.org (Dec 9, 2024), p. n/a
Autore principale:	He, Yong
Altri autori:	Li, Zeyu, Liu, Dong, Qin, Kangxiang, Xie, Jiahui
Pubblicazione:	Cornell University Library, arXiv.org
Soggetti:	Knowledge management Matrices (mathematics) Target acquisition Principal components analysis Subspaces Statistical analysis Statistical inference Knowledge representation
Accesso online:	Citation/Abstract Full text outside of ProQuest
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Descrizione
Abstract:	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.
ISSN:	2331-8422
Fonte:	Engineering Database