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  • Torch Dimensionality Reduction
  • User Guide
  • API and Modules
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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
  • 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

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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