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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.
|Title of host publication||Data Science|
|Editors||Francesco Palumbo, Angela Montanari, Maurizio Vichi|
|Number of pages||12|
|Publication status||Published - 5 Jul 2017|
|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)
|Name||Studies in Classification, Data Analysis, and Knowledge Organization|
|Conference||15th Conference of the International Federation of Classification Societies|
|Abbreviated title||IFCS 2015|
|Period||6/07/15 → 8/07/15|
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- 1 Oral presentation