Deep Recursive Embedding for High-Dimensional Data
Project description
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
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()
y = dre.fit_transform(x)
Run test_MNIST.py
to check the embedding procedure of MNIST dataset.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for DRE-1.1.10-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 753d3cbf2d3a54b8a91f39a9ab115e06dbd5c28bb8a9f43a59b637a75745d5f5 |
|
MD5 | c5ae5a1f0a68c694d9f9975db753aa26 |
|
BLAKE2b-256 | 0466633d1a67c0f1a5fc93366b8c4cbcd8fbc9c4019058e39ad3de6bc574d110 |