trans_mean

Transfer learning for mean estimation.

Transfer learning, aiming to use auxiliary domains to help improve learning of the target domain of interest when multiple heterogeneous datasets are available, has been a hot topic in statistical machine learning. The recent transfer learning methods with statistical guarantees mainly focus on the overall parameter transfer for supervised models in the ideal case with the informative auxiliary domains with overall similarity. In contrast, transfer learning for unsupervised graph learning is in its infancy and largely follows the idea of overall parameter transfer as for supervised learning.
In this package, the transfer learning for several complex graphical models is implemented, including Tensor Gaussian graphical models, non-Gaussian directed acyclic graph (DAG), and Gaussian graphical mixture models. Notably, this package promotes local transfer at node-level and subgroup-level in DAG structural learning and Gaussian graphical mixture models, respectively, which are more flexible and robust than the existing overall parameter transfer. As by-products, transfer learning for undirected graphical model (precision matrix) via D-trace loss, transfer learning for mean vector estimation, and single non-Gaussian learning via topological layer method are also included in this package.
Moreover, the aggregation of auxiliary information is an important issue in transfer learning, and this package provides multiple user-friendly aggregation methods, including sample weighting, similarity weighting, and most informative selection.
(Note: the transfer for tensor GGM has been temporarily removed in the current version as its dependent R package Tlasso has been archived. The historical version TransGraph_1.0.0.tar.gz can be downloaded at <https://cran.r-project.org/src/contrib/Archive/TransGraph/>)
Reference:
Ren, M., Zhen Y., and Wang J. (2024) <https://jmlr.org/papers/v25/22-1313.html> "Transfer learning for tensor graphical models".
Ren, M., He X., and Wang J. (2023) <doi:10.48550/arXiv.2310.10239> "Structural transfer learning of non-Gaussian DAG".
Zhao, R., He X., and Wang J. (2022) <https://jmlr.org/papers/v23/21-1173.html> "Learning linear non-Gaussian directed acyclic graph with diverging number of nodes".

Mingyang Ren

TransGraph

Transfer Graph Learning

Ruixuan Zhao

Xin He

Junhui Wang

trans_mean function

<dl><dt>t.mean.m</dt>
<dd>The estimated target p-dimensional mean vector, where p is mean dimension.</dd>
<dt>A.mean</dt>
<dd>A K*p matrix with the k-th row being the estimated p-dimensional mean vector of the k-th auxiliary domain.</dd>
<dt>n</dt>
<dd>The target sample size.</dd>
<dt>clambda</dt>
<dd>The coefficients set in tuning parameters used in transfer learning for mean eatimation, and the default setting is clambda.m * sqrt( log(p) / n ).</dd></dl>

Arguments

Mingyang Ren <a href="/link/renmingyang17%40mails.ucas.ac.cn?package=TransGraph&version=1.1.0" data-mini-rdoc="TransGraph::renmingyang17@mails.ucas.ac.cn">renmingyang17@mails.ucas.ac.cn</a>.

Author

Transfer learning for mean estimation. — trans_mean

<dl>

<dt>t.mean.m</dt>
<dd>The estimated target p-dimensional mean vector, where p is mean dimension.</dd>


<dt>A.mean</dt>
<dd>A K*p matrix with the k-th row being the estimated p-dimensional mean vector of the k-th auxiliary domain.</dd>


<dt>n</dt>
<dd>The target sample size.</dd>


<dt>clambda</dt>
<dd>The coefficients set in tuning parameters used in transfer learning for mean eatimation, and the default setting is clambda.m * sqrt( log(p) / n ).</dd>

</dl>

Mingyang Ren <a href='mailto:renmingyang17@mails.ucas.ac.cn'>renmingyang17@mails.ucas.ac.cn</a>.

trans_mean: Transfer learning for mean estimation.

Description

Usage

Value

Arguments

Author

References