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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
Categories
Data VisualizationLicense
MIT LicenseFollow ManifoldLearning
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