Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models

• URL: http://arxiv.org/abs/2402.10229v1
• Date: Thu, 8 Feb 2024 19:34:24 GMT
• Title: Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
• Authors: Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa, Vaibhav Rajan
• Abstract summary: textttMixture-Models is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants. It streamlines the implementation and analysis of these models using various first/second order optimization routines. The library provides user-friendly model evaluation tools, such as BIC, AIC, and log-likelihood estimation.
• Score: 4.60168321737677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: \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.

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