Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

JELSR_Feature_Selection

Project description

# JELSR Unsupervised Feature Selection of Joint Embedding Sparse Regression Analysis. # Summary The function JELSR follows the paper “Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection”(2014) by Chenping Hou, Feiping Nie, Xuelong Li, Dongyun Yi and Yi Wu

# Why a new package The JELSR feature selection approach is of great importance and is one of the most popular approaches in feature selection of unsupervised clustering problem.

The Package closely follows the paper and enables users to choose key parameters in the algrithm. It would be helpful to people interested in the method and could be directly applied to numpy array dataset.

The paper is relative new and no existing package designed for JELSR in python yet.

# Reference “Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection”(2014) by Chenping Hou, Feiping Nie, Xuelong Li, Dongyun Yi and Yi Wu

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for JELSR, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size JELSR-0.0.1.tar.gz (3.6 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page