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.
Features
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
Resumable training
Arbitrary neural network backbones
Customizable neighbour retrieval
Callbacks and Tensorboard integration
The latest development version is on github.
Get Started
Using ivis
Examples
Applications
Benchmarks
API Reference