Deep Recursive Embedding

Deep Recursive Embedding (DRE) is a novel demensionality reduction method based on a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by a recursive training strategy. DRE makes use of the latent data representations for boosted embedding performance.

Lab github DRE page: Tao Lab

Maintainer's github DRE page: Xinrui Zu

MNIST embedding result

Installation

DRE can be installed with a simple PyPi command:

pip install DRE

The pre-requests of DRE are:

numpy >= 1.19 scikit-learn >= 0.16 matplotlib numba >= 0.34 torch >= 1.0

How to use DRE

DRE follows the form of Scikit-learn APIs, whose fit_transform function is for returning the embedding result and fit for the whole model:

from DRE import DeepRecursiveEmbedding

dre = DeepRecursiveEmbedding()
# return the embedding result:
y = dre.fit_transform(x)
# or return the whole model:
dre.fit(x)

Copy and run test_mnist.py or test_mnist.ipynb to check the embedding procedure of MNIST dataset.