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Related Experiment Videos

Local Intrinsic Dimension Estimation by Generalized Linear Modeling.

Hideitsu Hino1, Jun Fujiki2, Shotaro Akaho3

  • 1Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan hinohide@cs.tsukuba.ac.jp.

Neural Computation
|April 15, 2017
PubMed
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We developed a new method for estimating intrinsic dimensions. This approach accurately estimates local dimensions and performs comparably to existing methods for global dimension estimation.

Area of Science:

  • Data Science
  • Machine Learning
  • Dimensionality Reduction

Background:

  • Estimating intrinsic dimensions is crucial for understanding complex datasets.
  • Existing methods for local intrinsic dimension estimation have limitations.

Purpose of the Study:

  • To propose a novel method for local intrinsic dimension estimation.
  • To evaluate the performance of the proposed method against conventional techniques.

Main Methods:

  • Fitting the power of distance and sample count within a ball to a regression model.
  • Utilizing the maximum likelihood method for local intrinsic dimension estimation.

Main Results:

  • The proposed method demonstrates comparable performance to conventional methods in global intrinsic dimension estimation.

Related Experiment Videos

  • The proposed method outperforms a conventional local dimension estimation method in experimental evaluations.

    • Conclusions:

    • The novel method provides an effective approach for local intrinsic dimension estimation.
    • This technique offers a promising alternative for analyzing high-dimensional data.