# Manifold_Learning

**Repository Path**: z2007c/Manifold_Learning

## Basic Information

- **Project Name**: Manifold_Learning
- **Description**: Worked examples about manifold learning using sklearn and jupyter
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-07-08
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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/

![alt text](https://raw.githubusercontent.com/cc-skuehn/Manifold_Learning/master/Manifold_Learning_Teaser.png)