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Breaking Dimensions: Adaptive Scoring with Sparse Grids

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Book cover Realtime Data Mining

Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

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Abstract

We introduce the concept of a sparse grid and show how this powerful approach to function space discretization may be employed to tackle high-dimensional machine learning problems of regression and classification. In particular, we address the issue of incremental computation of sparse grid regression coefficients so as to meet the requirements of realtime data mining. Conclusively, we present experimental results on real-world data sets.

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Authors and Affiliations

  1. Research and Development, prudsys AG, Berlin, Germany

    Alexander Paprotny

  2. Research and Development, prudsys AG, Chemnitz, Germany

    Michael Thess

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  1. Alexander Paprotny
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  2. Michael Thess
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Paprotny, A., Thess, M. (2013). Breaking Dimensions: Adaptive Scoring with Sparse Grids. In: Realtime Data Mining. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-01321-3_7

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