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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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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:
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
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author Mirkes, Evgeny M.
Bac, Jonathan
Fouché, Aziz
Stasenko, Sergey V.
Zinovyev, Andrei
Gorban, Alexander N.
author_facet Mirkes, Evgeny M.
Bac, Jonathan
Fouché, Aziz
Stasenko, Sergey V.
Zinovyev, Andrei
Gorban, Alexander N.
author_sort Mirkes, Evgeny M.
collection PubMed
description 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 common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.
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spelling pubmed-98582542023-01-21 Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data Mirkes, Evgeny M. Bac, Jonathan Fouché, Aziz Stasenko, Sergey V. Zinovyev, Andrei Gorban, Alexander N. Entropy (Basel) Article 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 common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains. MDPI 2022-12-24 /pmc/articles/PMC9858254/ /pubmed/36673174 http://dx.doi.org/10.3390/e25010033 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mirkes, Evgeny M.
Bac, Jonathan
Fouché, Aziz
Stasenko, Sergey V.
Zinovyev, Andrei
Gorban, Alexander N.
Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
title Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
title_full Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
title_fullStr Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
title_full_unstemmed Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
title_short Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
title_sort domain adaptation principal component analysis: base linear method for learning with out-of-distribution data
topic Article
url 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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