Zobrazeno 1 - 10
of 6 474
pro vyhledávání: '"Graphical Lasso"'
Autor:
Maruhashi, Koji1 (AUTHOR) maruhashi.koji@fujitsu.com, Kashima, Hisashi2 (AUTHOR), Miyano, Satoru3 (AUTHOR), Park, Heewon3,4 (AUTHOR) heewonn.park@gmail.com
Publikováno v:
Scientific Reports. 8/5/2024, Vol. 14 Issue 1, p1-17. 17p.
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph-lea
Externí odkaz:
http://arxiv.org/abs/2404.02621
In recent years, the availability of multi-omics data has increased substantially. Multi-omics data integration methods mainly aim to leverage different molecular data sets to gain a complete molecular description of biological processes. An attracti
Externí odkaz:
http://arxiv.org/abs/2403.18602
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract In complex systems, it’s crucial to uncover latent mechanisms and their context-dependent relationships. This is especially true in medical research, where identifying unknown cancer mechanisms and their impact on phenomena like drug resis
Externí odkaz:
https://doaj.org/article/f3ec428d6dcc4ac9890534c10d6745c1
The Graphical Lasso (GLasso) algorithm is fast and widely used for estimating sparse precision matrices (Friedman et al., 2008). Its central role in the literature of high-dimensional covariance estimation rivals that of Lasso regression for sparse e
Externí odkaz:
http://arxiv.org/abs/2403.12357
Recent developments in regularized Canonical Correlation Analysis (CCA) promise powerful methods for high-dimensional, multiview data analysis. However, justifying the structural assumptions behind many popular approaches remains a challenge, and fea
Externí odkaz:
http://arxiv.org/abs/2403.02979
Associated to each graph G is a Gaussian graphical model. Such models are often used in high-dimensional settings, i.e. where there are relatively few data points compared to the number of variables. The maximum likelihood threshold of a graph is the
Externí odkaz:
http://arxiv.org/abs/2312.03145
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Akademický článek
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Autor:
Wan, Phyllis, Zhou, Chen
In this paper we estimate the sparse dependence structure in the tail region of a multivariate random vector, potentially of high dimension. The tail dependence is modeled via a graphical model for extremes embedded in the Huesler-Reiss distribution
Externí odkaz:
http://arxiv.org/abs/2307.15004