Representational Transfer Learning for Matrix Completion
में बचाया:
| में प्रकाशित: | arXiv.org (Dec 9, 2024), p. n/a |
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| मुख्य लेखक: | |
| अन्य लेखक: | , , , |
| प्रकाशित: |
Cornell University Library, arXiv.org
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full text outside of ProQuest |
| टैग: |
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| सार: | 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. |
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| आईएसएसएन: | 2331-8422 |
| स्रोत: | Engineering Database |