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mtlgmm  

Unsupervised Multi-Task and Transfer Learning on Gaussian Mixture Models
View on CRAN: Click here


Download and install mtlgmm package within the R console
Install from CRAN:
install.packages("mtlgmm")

Install from Github:
library("remotes")
install_github("cran/mtlgmm")

Install by package version:
library("remotes")
install_version("mtlgmm", "0.1.0")



Attach the package and use:
library("mtlgmm")
Maintained by
Ye Tian
[Scholar Profile | Author Map]
First Published: 2022-10-31
Latest Update: 2022-10-31
Description:
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) .
How to cite:
Ye Tian (2022). mtlgmm: Unsupervised Multi-Task and Transfer Learning on Gaussian Mixture Models. R package version 0.1.0, https://cran.r-project.org/web/packages/mtlgmm. Accessed 08 Apr. 2025.
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Complete documentation for mtlgmm
Functions, R codes and Examples using the mtlgmm R package
Some associated functions: alignment . alignment_swap . data_generation . estimation_error . initialize . misclustering_error . mtlgmm . predict_gmm . tlgmm . 
Some associated R codes: alignment.R . alignment_swap.R . data_generation.R . estimation_error.R . funcs.R . initialize.R . misclustering_error.R . mtlgmm.R . predict_gmm.R . tlgmm.R .  Full mtlgmm package functions and examples
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