Sparse conditional copula models for structured output regression

DOI10.1016/j.patcog.2016.03.027zbMath1414.62134OpenAlexW2339230506MaRDI QIDQ2417829

Min-Young Kim

Publication date: 29 May 2019

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2016.03.027

zbMATH Keywords

Gaussian random fieldscopula modelssparse inverse covariance estimationmultiple output regression

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• glasso

Cites Work

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• A sparse conditional Gaussian graphical model for analysis of genetical genomics data
• Sparse inverse covariance estimation with the graphical lasso
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• An introduction to copulas. Properties and applications
• Support union recovery in high-dimensional multivariate regression
• Partial Gaussian Graphical Model Estimation
• An Introduction to Conditional Random Fields
• Multivariate Dispersion Models Generated From Gaussian Copula
• Gaussian Markov Random Fields
• A new look at the statistical model identification

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