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Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning

  • Sunil Kumar GuptaEmail author
  • Santu Rana
  • Dinh Phung
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)

Abstract

Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models.

Keywords

Root Mean Square Error Acute Myocardial Infarction Feature Subset Joint Modeling Task Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sunil Kumar Gupta
    • 1
    Email author
  • Santu Rana
    • 1
  • Dinh Phung
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Center for Pattern Recognition and Data AnalyticsDeakin UniversityGeelongAustralia

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