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A Dimensionality Reduction Method Based on Simple Linear Regressions

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Between Data Science and Applied Data Analysis

Abstract

After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression Methods, authors provide a different multivariate extension of the univariate PLS (1994) highlighting a different use and interpretation.

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© 2003 Springer-Verlag Berlin Heidelberg

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D’Ambra, L., Amenta, P., Lombardo, R. (2003). A Dimensionality Reduction Method Based on Simple Linear Regressions. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_23

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  • DOI: https://doi.org/10.1007/978-3-642-18991-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40354-8

  • Online ISBN: 978-3-642-18991-3

  • eBook Packages: Springer Book Archive

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