Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets into a com...

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Detalles Bibliográficos
Autores principales: Mirkes, Evgeny M., Bac, Jonathan, Fouché, Aziz, Stasenko, Sergey V., Zinovyev, Andrei, Gorban, Alexander N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858254/
https://www.ncbi.nlm.nih.gov/pubmed/36673174
http://dx.doi.org/10.3390/e25010033

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858254/
https://www.ncbi.nlm.nih.gov/pubmed/36673174
http://dx.doi.org/10.3390/e25010033

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