Estimate manifold dimensionality with LID
distance functions - High Dimensional Swiss Roll? (For Metric Learning/Dimensionality Reduction) - Cross Validated
How to create and plot a swiss roll using sklearn | by Tracyrenee | Geek Culture | Medium
Figure: Original Swiss roll dataset in 3 dimensions used for... | Download Scientific Diagram
Introduction to Machine Learning
a Swiss Roll Dataset
Swiss Roll And Swiss-Hole Reduction — scikit-learn 1.1.2 documentation
壁虎书8 Dimensionality Reduction - 羊小羚 - 博客园
sklearn.datasets.make_swiss_roll() scikit-learn官方教程 _w3cschool
Swiss Roll reduction with LLE — scikit-learn 0.23.2 documentation
Swiss Roll and SNE
GitHub - hamidkarimi/GAN-SwissRoll: A simple generative adversarial neural network using Tensorflow applied on a toy Swiss Roll dataset
Swiss roll data set. Fig. 11. Three-dimensional clusters data set. | Download Scientific Diagram
The Swiss Roll Matching Example
A ) The “Swiss roll” data used by Tenenbaum et al . ( 1 ) to... | Download Scientific Diagram
ch08 dimensionality reduction.md · Scikit and Tensorflow Workbooks (bjpcjp)
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach
Swiss Roll reduction with LLE — scikit-learn 0.11-git documentation
Dimensionality Reduction: A Comparative Review
The classic swiss roll data set — pydiffmap 0.2.0.1 documentation
Isomap Embedding — An Awesome Approach to Non-linear Dimensionality Reduction | by Saul Dobilas | Towards Data Science
Ehsan Amid on Twitter: "While t-SNE and UMAP are excellent methods for visualizing your data, sometimes the global structure, e.g., continuity of the data manifold, is better preserved using TriMap. See an
a Swiss Roll Dataset
tSNE vs PCA – The Kernel Trip
GitHub - majdjamal/manifold_learning: Showcasing Manifold Learning with ISOMAP, and compare the model to other transformations, such as PCA and MDS.
