A Robust EM Clustering Algorithm for Gaussian Mixture Models

Description

Python implementation of Robust EM Clustering for Gaussian Mixture Models[1]. (Click here to view the paper for more detail.)

• robustgmm.robustgmm

  Scikit-learn API style for Robust GMM

• robustgmm.generator

  Generator for synthetic data from mixture of gaussian.

For more detail to use, see the example below or paper_example.py

• Reference

  MS Yang, A robust EM clustering algorithm for gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961

Install

1. Install from PyPI

   pip install robustgmm

2. Install from Github

   pip install git+https://github.com/HongJea-Park/robust_EM_for_gmm.git

Example

All examples are conducted to compare with the experimental results of the paper.

# For more detail, refer ./test/paper_example.py
import numpy as np

from robustgmm import RobustGMM
from robustgmm import Generator_Multivariate_Normal


# Generate data from 2 multivariate normal distribution with fixed random seed
np.random.seed(0)
real_means = np.array([[.0, .0], [20, .0]])
real_covs = np.array([[[1, .0], [.0, 1]],
                      [[9, .0], [.0, 9]]])
mix_prob = np.array([.5, .5])
generator = Generator_Multivariate_Normal(means=real_means,
                                          covs=real_covs,
                                          mix_prob=mix_prob)
X = generator.get_sample(800)

# GMM using robust EM Algorithm
rgmm = RobustGMM()
rgmm.fit(X)

Figures for each examples in paper

1. Example 1

   

2. Example 2

   

3. Example 3

   

4. Example 4

   

5. Example 5

   

6. Example 6

   

7. Example 7

   

8. Computational time cost