Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity

Abstract Identifying structure underlying high-dimensional data is a common challenge across scientific disciplines. We revisit correspondence analysis (CA), a classical method revealing such structures, from a network perspective. We present the poorly-known equivalence of CA to spectral clustering...

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Autores principales:	Alje van Dam, Mark Dekker, Ignacio Morales-Castilla, Miguel Á. Rodríguez, David Wichmann, Mara Baudena
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4fb9c06fecf040749cdc0863644aa633
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spelling	oai:doaj.org-article:4fb9c06fecf040749cdc0863644aa6332021-12-02T17:39:23ZCorrespondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity10.1038/s41598-021-87971-92045-2322https://doaj.org/article/4fb9c06fecf040749cdc0863644aa6332021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87971-9https://doaj.org/toc/2045-2322Abstract Identifying structure underlying high-dimensional data is a common challenge across scientific disciplines. We revisit correspondence analysis (CA), a classical method revealing such structures, from a network perspective. We present the poorly-known equivalence of CA to spectral clustering and graph-embedding techniques. We point out a number of complementary interpretations of CA results, other than its traditional interpretation as an ordination technique. These interpretations relate to the structure of the underlying networks. We then discuss an empirical example drawn from ecology, where we apply CA to the global distribution of Carnivora species to show how both the clustering and ordination interpretation can be used to find gradients in clustered data. In the second empirical example, we revisit the economic complexity index as an application of correspondence analysis, and use the different interpretations of the method to shed new light on the empirical results within this literature.Alje van DamMark DekkerIgnacio Morales-CastillaMiguel Á. RodríguezDavid WichmannMara BaudenaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alje van Dam Mark Dekker Ignacio Morales-Castilla Miguel Á. Rodríguez David Wichmann Mara Baudena Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
description	Abstract Identifying structure underlying high-dimensional data is a common challenge across scientific disciplines. We revisit correspondence analysis (CA), a classical method revealing such structures, from a network perspective. We present the poorly-known equivalence of CA to spectral clustering and graph-embedding techniques. We point out a number of complementary interpretations of CA results, other than its traditional interpretation as an ordination technique. These interpretations relate to the structure of the underlying networks. We then discuss an empirical example drawn from ecology, where we apply CA to the global distribution of Carnivora species to show how both the clustering and ordination interpretation can be used to find gradients in clustered data. In the second empirical example, we revisit the economic complexity index as an application of correspondence analysis, and use the different interpretations of the method to shed new light on the empirical results within this literature.
format	article
author	Alje van Dam Mark Dekker Ignacio Morales-Castilla Miguel Á. Rodríguez David Wichmann Mara Baudena
author_facet	Alje van Dam Mark Dekker Ignacio Morales-Castilla Miguel Á. Rodríguez David Wichmann Mara Baudena
author_sort	Alje van Dam
title	Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
title_short	Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
title_full	Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
title_fullStr	Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
title_full_unstemmed	Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
title_sort	correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4fb9c06fecf040749cdc0863644aa633
work_keys_str_mv	AT aljevandam correspondenceanalysisspectralclusteringandgraphembeddingapplicationstoecologyandeconomiccomplexity AT markdekker correspondenceanalysisspectralclusteringandgraphembeddingapplicationstoecologyandeconomiccomplexity AT ignaciomoralescastilla correspondenceanalysisspectralclusteringandgraphembeddingapplicationstoecologyandeconomiccomplexity AT miguelarodriguez correspondenceanalysisspectralclusteringandgraphembeddingapplicationstoecologyandeconomiccomplexity AT davidwichmann correspondenceanalysisspectralclusteringandgraphembeddingapplicationstoecologyandeconomiccomplexity AT marabaudena correspondenceanalysisspectralclusteringandgraphembeddingapplicationstoecologyandeconomiccomplexity
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Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity

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