Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited

We introduce a data-dependent regularization problem which uses the geometry structure of the data to learn functions from incomplete data. We show another proof of the standard representer theorem when introducing the problem. At the end of the paper, two applications in image processing are used t...

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Detalles Bibliográficos
Autor principal: Zou, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304014/
http://dx.doi.org/10.1007/978-3-030-50420-5_23
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author Zou, Qing
author_facet Zou, Qing
author_sort Zou, Qing
collection PubMed
description We introduce a data-dependent regularization problem which uses the geometry structure of the data to learn functions from incomplete data. We show another proof of the standard representer theorem when introducing the problem. At the end of the paper, two applications in image processing are used to illustrate the function learning framework.
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spelling pubmed-73040142020-06-19 Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited Zou, Qing Computational Science – ICCS 2020 Article We introduce a data-dependent regularization problem which uses the geometry structure of the data to learn functions from incomplete data. We show another proof of the standard representer theorem when introducing the problem. At the end of the paper, two applications in image processing are used to illustrate the function learning framework. 2020-05-22 /pmc/articles/PMC7304014/ http://dx.doi.org/10.1007/978-3-030-50420-5_23 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Zou, Qing
Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited
title Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited
title_full Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited
title_fullStr Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited
title_full_unstemmed Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited
title_short Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited
title_sort learning functions using data-dependent regularization: representer theorem revisited
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304014/
http://dx.doi.org/10.1007/978-3-030-50420-5_23
work_keys_str_mv AT zouqing learningfunctionsusingdatadependentregularizationrepresentertheoremrevisited

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