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Semi-supervised Linear Discriminant Analysis Using Moment Constraints

  • Marco Loog
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7081)

Abstract

A semi-supervised version of Fisher’s linear discriminant analysis is presented. As opposed to virtually all other approaches to semi-supervision, no assumptions on the data distribution are made, apart from the ones explicitly or implicitly present in standard supervised learning. Our approach exploits the fact that the parameters that are to be estimated in linear discriminant analysis fulfill particular relations that link label-dependent with label-independent quantities. In this way, the later type of parameters, which can be estimated based on unlabeled data, impose constraints on the former and lead to a reduction in variability of the label dependent estimates. As a result, the performance of our semi-supervised linear discriminant is expected to improve over that of its supervised equal and typically does not deteriorate with increasing numbers of unlabeled data.

Keywords

Linear Discriminant Analysis Unlabeled Data Unlabeled Instance Moment Constraint Semisupervised Learning 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marco Loog
    • 1
  1. 1.Pattern Recognition LaboratoryDelft University of TechnologyThe Netherlands

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