ivis: structure preserving dimensionality reduction¶
ivis is a machine learning algorithm for reducing dimensionality of very large datasets.
ivis preserves global data structures in a low-dimensional space, adds new data points to existing embeddings using a parametric mapping function, and scales linearly to millions of observations. The algorithm is described in detail in Structure-preserving visualisation of high dimensional single-cell datasets.
The latest development version is on github.
- Python Package
- R Package
- Hyperparameter Selection
- Supervised Dimensionality Reduction
- Semi-supervised Dimensionality Reduction