Sparse conditional copula models for structured output regression

作者：

Highlights：

• Sparse non-linear, non-Gaussian density modeling by conditional copula.

• Loose output correlation estimation by sparse copula inverse covariance learning.

• Efficient alternating optimization method for marginals and copula.

• Superior to existing multiple output regression methods on several datasets.

摘要

Highlights•Sparse non-linear, non-Gaussian density modeling by conditional copula.•Loose output correlation estimation by sparse copula inverse covariance learning.•Efficient alternating optimization method for marginals and copula.•Superior to existing multiple output regression methods on several datasets.

论文关键词：Multiple output regression,Sparse inverse covariance estimation,Copula models,Gaussian random fields

论文评审过程：Received 17 March 2015, Revised 1 February 2016, Accepted 19 March 2016, Available online 4 April 2016, Version of Record 17 July 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.03.027