Metadata-Version: 1.2
Name: cne-learn
Version: 0.0.dev0
Summary: Contrastive neighbor embeddings (CNE) for dimensionality reduction and clustering
Home-page: https://github.com/jgraving/cne
Author: Jacob Graving <jgraving@gmail.com>
Author-email: jgraving@gmail.com
Maintainer: Jacob Graving <jgraving@gmail.com>
Maintainer-email: jgraving@gmail.com
License: Apache 2.0
Download-URL: https://github.com/jgraving/cne.git
Description: CNE is a probabilistic self-supervised deep learning model for compressing high-dimensional data to a low-dimensional embedding. CNE is a general-purpose algorithm that works with multiple types of data including images, time series, and tabular data. It uses the InfoNCE objective, a variational bound on mutual information, to improve local structure preservation in the compressed latent space and simultaneously learns a cluster distribution (a prior over the latent embedding) during optimization. Overlapping clusters are automatically combined by optimizing a variational upper bound on entropy, so the number of clusters does not have to be specified manually — provided the number of initial clusters is large enough. CNE produces embeddings with similar quality to existing dimensionality reduction methods; can detect outliers; scales to large, out-of-core datasets; and can easily add new data to an existing embedding/clustering.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
