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  • Torch Dimensionality Reduction
  • User Guide
  • API and Modules
  • Gallery
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    • Contributing
    • Bibliography
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  • Torch Dimensionality Reduction
  • User Guide
  • API and Modules
  • Gallery
  • Releases
  • Contributing
  • Bibliography
  • GitHub
  • PyPI

Section Navigation

  • Entropic Affinities can adapt to varying noise levels
  • PCA via SVD and via AffinityMatcher
  • TSNE embedding of the swiss roll dataset
  • TSNE vs COSNE : Euclidean vs Hyperbolic
  • Incremental PCA on GPU
  • Neighbor Embedding on genomics & equivalent affinity matcher formulation
  • Gallery

Gallery#

All the examples have a download link at the end. You can load the example’s notebook on Google Colab and run them by adding the line

pip install git+https://github.com/torchdr/torchdr.git#egg=torchdr

to the top of the notebook.

Affinities#

Entropic Affinities can adapt to varying noise levels

Entropic Affinities can adapt to varying noise levels

Basics#

PCA via SVD and via AffinityMatcher

PCA via SVD and via AffinityMatcher

TSNE embedding of the swiss roll dataset

TSNE embedding of the swiss roll dataset

TSNE vs COSNE : Euclidean vs Hyperbolic

TSNE vs COSNE : Euclidean vs Hyperbolic

Incremental PCA on GPU

Incremental PCA on GPU

Neighbor Embedding on genomics & equivalent affinity matcher formulation

Neighbor Embedding on genomics & equivalent affinity matcher formulation

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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Entropic Affinities can adapt to varying noise levels

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