Incremental Distributed Weighted Class Discriminant Analysis on Interval-Valued Emitter Parameters

  • Xin Xu
  • Wei Wang
  • Jiaheng Lu
  • Jin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9403)


In the age of big data, the emitter parameter measurement data is generally characteristic of uncertainty in the form of normally-distributed intervals, enormous size and continuous growth. However, existing interval-valued data analysis methods generally assume a uniform distribution instead and are unable to adapt to the rapid growth of volume. To address the above problems, we have brought forward an incremental distributed weighted class discriminant analysis method on interval-valued emitter parameters. Extensive experiments indicate that our method is able to cope with these new characteristics effectively.


Fuzzy pattern mining Emitter identification Class discriminant analysis Incremental learning Distributed computing Signal processing 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Science and Technology on Information System Engineering LaboratoryNRIEENanjingChina
  2. 2.State Key Laboratory for Novel Software and TechnologyNanjing UniversityNanjingChina
  3. 3.Key Laboratory of Data Engineering and Knowledge EngineeringRenmin UniversityBeijingChina
  4. 4.Department of Energy Plant Research LabMichigan State UniversityEast LansingUSA
  5. 5.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

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