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Locally Linear Embedding

In: Applied Multivariate Statistical Analysis

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics)

  • Léopold Simar

    (Université Catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences)

  • Matthias R. Fengler

    (University of St. Gallen, School of Economics and Political Science)

Abstract

Traditional visualization techniques can hardly be used to analyze very high-dimensional data. Consider, for example, applying the Chernoff-Flury faces of Chap. 1.5 to identify patterns of interest within data that represent images. In this case, the pixel intensity vectors that embody the image can easily have thousands of dimensions. A similar issue occurs in textual analysis where word-count vectors are employed to describe documents. In these situations, Chernoff-Flury faces can no longer be applied. Alternative techniques like Andrews’ curves and parallel coordinate plots will provide such a bad “signal-to-ink” ratio that human interpretation of the graphical representation of the data is impossible.

Suggested Citation

  • Wolfgang Karl Härdle & Léopold Simar & Matthias R. Fengler, 2024. "Locally Linear Embedding," Springer Books, in: Applied Multivariate Statistical Analysis, edition 0, chapter 0, pages 557-568, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-63833-6_21
    DOI: 10.1007/978-3-031-63833-6_21
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