Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic

Sally Ghanem, Hamid Krim, Hamilton Scott Clouse, Wesam Sakla

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the-apcode.

Original languageEnglish
Article number8424176
Pages (from-to)5947-5956
Number of pages10
JournalIEEE Transactions on Image Processing
Volume27
Issue number12
DOIs
StatePublished - Dec 2018
Externally publishedYes

Keywords

  • Dimensionality reduction
  • classification
  • divergence measures
  • nearest neighbor graph
  • pattern recognition

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