Evidence transfer approach of combining categorical evidence to improve clustering tasks

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Evidence transfer approach of combining categorical evidence to improve clustering tasks

Official code repository of “Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence”. It includes the implementation of all experiments (using TensorFlow), as well as the scripts used to produce the figures displayed in the paper. It also includes all neural network configurations described using ANNETT-O. The ANNETT-O description can be found here.

Setup

evitrac/ $ pip install -r requirements.txt

Running experiments

Create necessary datasets:

evitrac/mkdata/ $ ./mk.py (mnist|cifar|ng20|reuters)

Create initial latent space:

evitrac/ $ ./train.py ini/(mnist|cifar|20ng|reu100k)/px.ini 0

Create latent categorical evidence variables:

evitrac/ $ run/(mnist|cifar|20ng|reu100k)/init_eviae.sh

mrun scripts were created in order to run in parallel, but can be run sequentially:

evitrac/ $ run/(mnist|cifar|20ng|reu100k)/mrun(0|1|2|3).sh

For triple evidence used in CIFAR-10:

evitrac/ $ run/cifar/triple(0|1|2|3).sh