Skip to content

ngoix/EMMV_benchmarks

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

35 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

EMMV_benchmarks

How to evaluate unsupervised Anomaly Detection algorithms?

author: Nicolas Goix, [email protected]

This is the code associated with ICML workshop paper https://arxiv.org/abs/1607.01152

-File em_bench.py evaluates the algorithms using EM and MV based criteria, without sub-sampling features nor averaging. It does not work in high dimensions.

-File em_bench_high.py makes use of sub-sampling (along features) and averaging, extending the use of these criteria to high-dimensional datasets.

-Basic EM and MV calculation is implemented in em.py

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages