| Name : R-mtlgmm
| |
| Version : 0.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp154.9.1
| Date : 2024-07-04 12:12:31
|
| Group : Development/Libraries/Other
| Source RPM : R-mtlgmm-0.1.0-lp154.9.1.src.rpm
|
| Size : 0.11 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Unsupervised Multi-Task and Transfer Learning on Gaussian Mixture Models
|
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) < arXiv:2209.15224>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.4/x86_64 |
