Transductive Regression for Data with Latent Dependence Structure

Nico Gornitz, Luiz Alberto Lima, Luiz Eduardo Varella, Klaus Robert Muller, Shinichi Nakajima

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Analyzing data with latent spatial and/or temporal structure is a challenge for machine learning. In this paper, we propose a novel nonlinear model for studying data with latent dependence structure. It successfully combines the concepts of Markov random fields, transductive learning, and regression, making heavy use of the notion of joint feature maps. Our transductive conditional random field regression model is able to infer the latent states by combining limited labeled data of high precision with unlabeled data containing measurement uncertainty. In this manner, we can propagate accurate information and greatly reduce uncertainty. We demonstrate the usefulness of our novel framework on generated time series data with the known temporal structure and successfully validate it on synthetic as well as real-world offshore data with the spatial structure from the oil industry to predict rock porosities from acoustic impedance data.

Original languageEnglish
Pages (from-to)2743-2756
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number7
Publication statusPublished - 2018 Jul


  • Conditional random fields (CRFs)
  • non-independent and identically distributed (IID)
  • ridge regression (RR)
  • semisupervised and transductive learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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