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- W4290463764 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." @default.
- W4290463764 created "2022-08-08" @default.
- W4290463764 creator A5000885553 @default.
- W4290463764 date "2015-12-13" @default.
- W4290463764 modified "2023-09-24" @default.
- W4290463764 title "Big Data Scaling through Metric Mapping: Exploiting the Remarkable Simplicity of Very High Dimensional Spaces using Correspondence Analysis" @default.
- W4290463764 doi "https://doi.org/10.48550/arxiv.1512.04052" @default.
- W4290463764 hasPublicationYear "2015" @default.
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