Kernel Density Estimation (KDE) is a widely used non-parametric technique to estimate a probability density function. mlpack already had an implementation of this technique and the goal of this project is to improve the existing codebase, making it faster and more flexible.

These improvements include:

  • Improvements to the KDE space-partitioning trees algorithm.
  • Cases in which data dimensionality is high and metric evaluations are computationally expensive.

Organization

Student

Roberto Hueso Gomez

Mentors

  • Ryan Curtin
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2019