| Name : R-TransGraph
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| Version : 1.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.2.4
| Date : 2024-06-14 11:51:52
|
| Group : Development/Libraries/Other
| Source RPM : R-TransGraph-1.0.1-lp153.2.4.src.rpm
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| Size : 1.15 MB
| |
| Packager : (none)
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| Summary : Transfer Graph Learning
|
Description :
Transfer learning, aiming to use auxiliary domains to help improve
learning of the target domain of interest when multiple heterogeneous
datasets are available, has always 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. Reference: Ren,
M., Zhen Y., and Wang J. (2022) < arXiv:2211.09391> \"Transfer learning
for tensor graphical models\". Ren, M., He X., and Wang J. (2023)
< 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\".
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
