Manifold_Learning Repository
Worked examples about manifold learning using sklearn and jupyter - It's all a bit Work-In-Progress so the "should be working" notebooks are
- Manifold_S_Dataset.ipynb
- Manifold_S_Dataset_Morphed.ipynb
- Manifold_Sphere_Dataset.ipynb
- Manifold_Handwritten_Digits_Dataset.ipynb
- Manifold_tSNE_UMAP_Advanced.ipynb
- Manifold_Handwritten_Digits_Classification_WIP.ipynb
Slides
https://de.slideshare.net/StefanKhn4
Alternatively, there are some pdf versions of related talks in the repo, the latest version being Mcubed_20181016.pdf
In the latest slides there are even more links to useful resources - most probably the full list.
Methods covered (well, mentioned and used)
Dimensionality Reduction
- PCA - Principal Component Analysis
- GRP - Gaussian Random Projections
- SRP - Sparse Random Projections
Standard Manifold Learning
- LLE - Locally Linear Embedding (multiple variants)
- Isomap - Isometric Feature Mapping
- MDS - Multi-Dimensional Scaling
- Spectral Embedding (Laplacian Eigenmaps)
- LTSA - Local Tangent Space Alignment
Advanced Manifold Learning
- tSNE - t-Distributed Stochastic Neighbor Embedding
- UMAP - Uniform Manifold Approximation and Projection
Resources
Scikit-learn documentation
http://scikit-learn.org/stable/modules/manifold.html
http://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html
http://scikit-learn.org/stable/auto_examples/manifold/plot_manifold_sphere.html
http://scikit-learn.org/stable/modules/random_projection.html
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
Github repo with worked examples
https://github.com/cc-skuehn/Manifold_Learning
Jupyter Lab
https://jupyterlab.readthedocs.io/en/stable/index.html
Citation
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
tSNE
Original tSNE paper http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
Distill is a great resource, not only for understanding tSNE! https://distill.pub/2016/misread-tsne/
Linear tSNE for the Web https://graphics.tudelft.nl/Publications-new/2018/PMHLEV18/pezotti.pdf
UMAP
Original UMAP paper https://arxiv.org/pdf/1802.03426.pdf
Github repo for UMAP (Python) https://github.com/lmcinnes/umap
You need to install UMAP first, follow the instructions: https://github.com/lmcinnes/umap#installing
Documentation https://umap-learn.readthedocs.io/en/latest/basic_usage.html
Author / Contact / Questions
Visit me on XING
https://www.xing.com/profile/Stefan_Kuehn46
But I am on LinkedIn as well...
https://www.linkedin.com/in/stefan-k%C3%BChn-020a34119/
