Big data scaling through metric mapping: exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis

Fionn Murtagh

doi:10.1007/978-3-319-55723-6_23

Big data scaling through metric mapping: exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis

Fionn Murtagh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

We present new findings in regard to data analysis in very high dimensional spaces. We use dimensionalities up to around one million. A particular benefit of Correspondence Analysis is its suitability for carrying out an orthonormal mapping, or scaling, of power law distributed data. Power law distributed data are found in many domains. Correspondence factor analysis provides a latent semantic or principal axes mapping. Our experiments use data from digital chemistry and finance, and other statistically generated data.

Original language	English
Title of host publication	Data Science
Editors	Francesco Palumbo, Angela Montanari, Maurizio Vichi
Pages	295-306
Number of pages	12
Edition	195089
ISBN (Electronic)	9783319557236
DOIs	https://doi.org/10.1007/978-3-319-55723-6_23
Publication status	Published - 5 Jul 2017
Externally published	Yes
Event	15th Conference of the International Federation of Classification Societies - Bologna, Italy Duration: 6 Jul 2015 → 8 Jul 2015 Conference number: 15 https://studylib.net/doc/10711915/ifcs-2015-call-for-papers-conference-of-the-international... (Link to Call for Papers) https://web.archive.org/web/20160717055549/http://ifcs.boku.ac.at/_conference/public/conferences/1/schedConfs/1/program-en_US.pdf (Program)

Publication series

Name	Studies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)	1431-8814

Conference

Conference	15th Conference of the International Federation of Classification Societies
Abbreviated title	IFCS 2015
Country/Territory	Italy
City	Bologna
Period	6/07/15 → 8/07/15
Internet address	https://studylib.net/doc/10711915/ifcs-2015-call-for-papers-conference-of-the-international... (Link to Call for Papers) https://web.archive.org/web/20160717055549/http://ifcs.boku.ac.at/_conference/public/conferences/1/schedConfs/1/program-en_US.pdf (Program)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-55723-6_23Licence: Unspecified

Cite this

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title = "Big data scaling through metric mapping: exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis",

abstract = "We present new findings in regard to data analysis in very high dimensional spaces. We use dimensionalities up to around one million. A particular benefit of Correspondence Analysis is its suitability for carrying out an orthonormal mapping, or scaling, of power law distributed data. Power law distributed data are found in many domains. Correspondence factor analysis provides a latent semantic or principal axes mapping. Our experiments use data from digital chemistry and finance, and other statistically generated data.",

author = "Fionn Murtagh",

year = "2017",

month = jul,

day = "5",

doi = "10.1007/978-3-319-55723-6_23",

language = "English",

isbn = "9783319557229",

series = "Studies in Classification, Data Analysis, and Knowledge Organization",

pages = "295--306",

editor = "Francesco Palumbo and Angela Montanari and Maurizio Vichi",

booktitle = "Data Science",

edition = "195089",

note = "15th Conference of the International Federation of Classification Societies, IFCS 2015 ; Conference date: 06-07-2015 Through 08-07-2015",

url = "https://studylib.net/doc/10711915/ifcs-2015-call-for-papers-conference-of-the-international..., https://web.archive.org/web/20160717055549/http://ifcs.boku.ac.at/_conference/public/conferences/1/schedConfs/1/program-en_US.pdf",

}

Murtagh, F 2017, Big data scaling through metric mapping: exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis. in F Palumbo, A Montanari & M Vichi (eds), Data Science. 195089 edn, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 295-306, 15th Conference of the International Federation of Classification Societies, Bologna, Italy, 6/07/15. https://doi.org/10.1007/978-3-319-55723-6_23

Big data scaling through metric mapping : exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis. / Murtagh, Fionn.

Data Science. ed. / Francesco Palumbo; Angela Montanari; Maurizio Vichi. 195089. ed. 2017. p. 295-306 (Studies in Classification, Data Analysis, and Knowledge Organization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - We present new findings in regard to data analysis in very high dimensional spaces. We use dimensionalities up to around one million. A particular benefit of Correspondence Analysis is its suitability for carrying out an orthonormal mapping, or scaling, of power law distributed data. Power law distributed data are found in many domains. Correspondence factor analysis provides a latent semantic or principal axes mapping. Our experiments use data from digital chemistry and finance, and other statistically generated data.

AB - We present new findings in regard to data analysis in very high dimensional spaces. We use dimensionalities up to around one million. A particular benefit of Correspondence Analysis is its suitability for carrying out an orthonormal mapping, or scaling, of power law distributed data. Power law distributed data are found in many domains. Correspondence factor analysis provides a latent semantic or principal axes mapping. Our experiments use data from digital chemistry and finance, and other statistically generated data.

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DO - 10.1007/978-3-319-55723-6_23

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Y2 - 6 July 2015 through 8 July 2015

ER -

Big data scaling through metric mapping: exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis

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The New Science of Big Data Analytics, Based on the Geometry and the Topology of Complex Systems

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Big data scaling through metric mapping: exploiting the remarkable simplicity of very high dimensional spaces using correspondence analysis

Abstract

Publication series

Conference

UN SDGs

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The New Science of Big Data Analytics, Based on the Geometry and the Topology of Complex Systems

Cite this