scholarly journals High dimensional semiparametric latent graphical model for mixed data

2016 ◽  
Vol 79 (2) ◽  
pp. 405-421 ◽  
Author(s):  
Jianqing Fan ◽  
Han Liu ◽  
Yang Ning ◽  
Hui Zou
Keyword(s):  
Graphical Model ◽  
Mixed Data ◽  
High Dimensional

scholarly journals Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data

2017 ◽  
Vol 36 (28) ◽  
pp. 4548-4569 ◽  
Author(s):  
D. McParland ◽  
C. M. Phillips ◽  
L. Brennan ◽  
H. M. Roche ◽  
I. C. Gormley
Keyword(s):  
Mixed Data ◽  
Joint Analysis ◽  
High Dimensional

scholarly journals Unsupervised Learning for Large Scale Data: The ATHLOS Project

2021 ◽  
Author(s):  
Petros Barmpas ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Panagiotis Anagnostou ◽  
Spiros Georgakopoulos ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Large Scale ◽  
High Dimensional Data ◽  
Experimental Studies ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Types ◽  
Unified Framework

1AbstractRecent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.

scholarly journals High-Dimensional Graphical Model Search with thegRapHDRPackage

2010 ◽  
Vol 37 (1) ◽  
Author(s):  
Gabriel C. G. Abreu ◽  
David Edwards ◽  
Rodrigo Labouriau
Keyword(s):  
Graphical Model ◽  
High Dimensional ◽  
Model Search

High dimensional Gaussian copula graphical model with FDR control

2017 ◽  
Vol 113 ◽  
pp. 457-474 ◽  
Author(s):  
Yong He ◽  
Xinsheng Zhang ◽  
Pingping Wang ◽  
Liwen Zhang
Keyword(s):  
Graphical Model ◽  
Gaussian Copula ◽  
High Dimensional

scholarly journals Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data

2019 ◽  
Vol 31 (6) ◽  
pp. 1183-1214 ◽  
Author(s):  
Suwa Xu ◽  
Bochao Jia ◽  
Faming Liang
Keyword(s):  
Computational Complexity ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Algorithm ◽  
Hill Climbing ◽  
Mixed Data ◽  
High Dimensional ◽  
Large Set ◽  
Worst Case ◽  
Markov Network

Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called p-learning algorithm, for learning moral graphs for high-dimensional Bayesian networks. The moral graph is a Markov network representation of the Bayesian network and also the key to construction of the Bayesian network for constraint-based algorithms. The consistency of the p-learning algorithm is justified under the small- n, large- p scenario. The numerical results indicate that the p-learning algorithm significantly outperforms the existing ones, such as the PC, grow-shrink, incremental association, semi-interleaved hiton, hill-climbing, and max-min hill-climbing. Under the sparsity assumption, the p-learning algorithm has a computational complexity of O(p2) even in the worst case, while the existing algorithms have a computational complexity of O(p3) in the worst case.

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