Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
<|reference_start|>Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models: \texttt{Mixture-Models} is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClust models, Mixture of Student's t distributions, etc. It streamlines the implementation and analysis of these models using various first/second order optimization routines such as Gradient Descent and Newton-CG through automatic differentiation (AD) tools. This helps in extending these models to high-dimensional data, which is first of its kind among Python libraries. The library provides user-friendly model evaluation tools, such as BIC, AIC, and log-likelihood estimation. The source-code is licensed under MIT license and can be accessed at \url{https://github.com/kasakh/Mixture-Models}. The package is highly extensible, allowing users to incorporate new distributions and optimization techniques with ease. We conduct a large scale simulation to compare the performance of various gradient based approaches against Expectation Maximization on a wide range of settings and identify the corresponding best suited approach.<|reference_end|>
@article{kasa2024mixture-models:,
    title={Mixture-Models: a one-stop Python Library for Model-based Clustering
  using various Mixture Models},
    author={Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa, Vaibhav Rajan},
    journal={arXiv preprint arXiv:2402.10229},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10229},
    primaryClass={stat.CO cs.LG}
}