A Julia package for manifold learning and nonlinear dimensionality reduction. Most of the methods use k-nearest neighbors method for constructing local subspace representation. By default, neighbors are computed from a distance matrix of a dataset. This is not an efficient method, especially, for large datasets.

Features

  • Documentation available
  • Examples available
  • Local tangent space alignment (LTSA)
  • t-Distributed Stochastic Neighborhood Embedding (t-SNE)
  • Hessian Eigenmaps (HLLE)
  • Locally Linear Embedding (LLE)

Project Samples

ManifoldLearning Screenshot 1

Project Activity

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Categories

Data Visualization

License

MIT License

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Additional Project Details

Programming Language

Julia

Related Categories

Julia Data Visualization Software

Registered

2023-12-08
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