Kernel parameter dependence in spatial factor analysis
Principal component analysis (PCA) [1] is often used for general feature generation and linear orthogonalization or compression by dimensionality reduction of correlated multivariate data, see Jolliffe [2] for a comprehensive description of PCA and related techniques. Schölkopf et al. [3] introduce...
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ftdtupubl:oai:pure.atira.dk:publications/fd642873-bbb1-4a27-8a5f-35b521f8a6f6 2023-12-24T10:17:11+01:00 Kernel parameter dependence in spatial factor analysis Nielsen, Allan Aasbjerg 2010 application/pdf https://orbit.dtu.dk/en/publications/fd642873-bbb1-4a27-8a5f-35b521f8a6f6 https://doi.org/10.1109/IGARSS.2010.5653545 https://backend.orbit.dtu.dk/ws/files/5557989/imm5855.pdf eng eng IEEE https://orbit.dtu.dk/en/publications/fd642873-bbb1-4a27-8a5f-35b521f8a6f6 urn:ISBN:978-1-4244-9564-1 info:eu-repo/semantics/openAccess Nielsen , A A 2010 , Kernel parameter dependence in spatial factor analysis . in IGARSS . IEEE , pp. 4240-4243 , 30th International Geoscience and Remote Sensing symposium , Honolulu, HI , United States , 25/07/2010 . https://doi.org/10.1109/IGARSS.2010.5653545 contributionToPeriodical 2010 ftdtupubl https://doi.org/10.1109/IGARSS.2010.5653545 2023-11-29T23:58:11Z Principal component analysis (PCA) [1] is often used for general feature generation and linear orthogonalization or compression by dimensionality reduction of correlated multivariate data, see Jolliffe [2] for a comprehensive description of PCA and related techniques. Schölkopf et al. [3] introduce kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements. Article in Journal/Newspaper Greenland Technical University of Denmark: DTU Orbit Greenland 2010 IEEE International Geoscience and Remote Sensing Symposium 4240 4243 |
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Open Polar |
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Technical University of Denmark: DTU Orbit |
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ftdtupubl |
language |
English |
description |
Principal component analysis (PCA) [1] is often used for general feature generation and linear orthogonalization or compression by dimensionality reduction of correlated multivariate data, see Jolliffe [2] for a comprehensive description of PCA and related techniques. Schölkopf et al. [3] introduce kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements. |
format |
Article in Journal/Newspaper |
author |
Nielsen, Allan Aasbjerg |
spellingShingle |
Nielsen, Allan Aasbjerg Kernel parameter dependence in spatial factor analysis |
author_facet |
Nielsen, Allan Aasbjerg |
author_sort |
Nielsen, Allan Aasbjerg |
title |
Kernel parameter dependence in spatial factor analysis |
title_short |
Kernel parameter dependence in spatial factor analysis |
title_full |
Kernel parameter dependence in spatial factor analysis |
title_fullStr |
Kernel parameter dependence in spatial factor analysis |
title_full_unstemmed |
Kernel parameter dependence in spatial factor analysis |
title_sort |
kernel parameter dependence in spatial factor analysis |
publisher |
IEEE |
publishDate |
2010 |
url |
https://orbit.dtu.dk/en/publications/fd642873-bbb1-4a27-8a5f-35b521f8a6f6 https://doi.org/10.1109/IGARSS.2010.5653545 https://backend.orbit.dtu.dk/ws/files/5557989/imm5855.pdf |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
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Greenland |
op_source |
Nielsen , A A 2010 , Kernel parameter dependence in spatial factor analysis . in IGARSS . IEEE , pp. 4240-4243 , 30th International Geoscience and Remote Sensing symposium , Honolulu, HI , United States , 25/07/2010 . https://doi.org/10.1109/IGARSS.2010.5653545 |
op_relation |
https://orbit.dtu.dk/en/publications/fd642873-bbb1-4a27-8a5f-35b521f8a6f6 urn:ISBN:978-1-4244-9564-1 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1109/IGARSS.2010.5653545 |
container_title |
2010 IEEE International Geoscience and Remote Sensing Symposium |
container_start_page |
4240 |
op_container_end_page |
4243 |
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1786205136988667904 |