ivis dimensionality reduction¶
ivis is a machine learning library for reducing dimensionality of very large datasets using Siamese Neural Networks.
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.
Unsupervised, semi-supervised, and fully supervised dimensionality reduction
Support for arbitrary datasets
- N-dimensional arrays
- Image files on disk
- Custom data connectors
In- and out-of-memory data processing
Arbitrary neural network backbones
Customizable neighbour retrieval
Callbacks and Tensorboard integration
The latest development version is on github.
- Unsupervised Dimensionality Reduction
- Supervised Dimensionality Reduction
- Semi-supervised Dimensionality Reduction
- Hyperparameter Selection