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Locally Balanced Incremental Hierarchical Discriminant Regression

  • Xiao Huang
  • Juyang Weng
  • Roger Calantone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)

Abstract

Incremental hierarchical discriminant regression faces several challenging issues: (a) a large input space with a small output space and (b) nonstationary statistics of data sequences. In the first case (a), there maybe few distinct labels in the output space while the input data distribute in a high dimensional space. In the second case (b), a tree has to be grown when only a limited data sequence has been observed. In this paper, we present the Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) algorithm. A novel node self-organization and spawning strategy is proposed to generate a more discriminant subspace by forming multiple clusters for one class. The algorithm was successfully applied to different kinds of data set.

Keywords

Linear Discriminant Analysis Face Image Regression Tree Input Space Output Space 
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 2003

Authors and Affiliations

  • Xiao Huang
    • 1
  • Juyang Weng
    • 1
  • Roger Calantone
    • 2
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.The Eli Broad College of BusinessMichigan State UniversityEast LansingUSA

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