beringresearch/ivis
Dimensionality reduction in very large datasets using Siamese Networks
Implements triplet loss-based Siamese networks optimized for millions of observations, supporting both unsupervised and supervised learning modes. Handles diverse data formats (numpy arrays, sparse matrices, HDF5) and feature types (categorical/continuous), with sklearn-compatible `transform` for incremental embedding of new points. Built on TensorFlow with GPU acceleration available, designed to preserve both local and global dataset structure better than t-SNE.
343 stars and 1,087 monthly downloads. Available on PyPI.
Stars
343
Forks
44
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 10, 2025
Monthly downloads
1,087
Commits (30d)
0
Dependencies
5
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