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Volume 32 Issue 11
Dec.  2010
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Luo Lei, Li Yue-Hua. Uncorrelated Discriminant Neighborhood Preserving Projections for Millimeter Wave Radar Target Recognition[J]. Journal of Electronics & Information Technology, 2010, 32(11): 2751-2754. doi: 10.3724/SP.J.1146.2009.01534
Citation: Luo Lei, Li Yue-Hua. Uncorrelated Discriminant Neighborhood Preserving Projections for Millimeter Wave Radar Target Recognition[J]. Journal of Electronics & Information Technology, 2010, 32(11): 2751-2754. doi: 10.3724/SP.J.1146.2009.01534

Uncorrelated Discriminant Neighborhood Preserving Projections for Millimeter Wave Radar Target Recognition

doi: 10.3724/SP.J.1146.2009.01534
  • Received Date: 2009-12-01
  • Rev Recd Date: 2010-03-15
  • Publish Date: 2010-11-19
  • A new algorithm named Uncorrelated Discriminant Neighborhood Preserving Projections (UDNPP) is proposed based on manifold learning. And UDNPP algorithm includes the advantages of Linear Discriminant Analysis (LDA) and Neighborhood Preserving Projections (NPP). Actually, UDNPP attempts to preserve the geometry of neighborhoods, while maximizing the between-class distance. Moreover, the features extracted are statistically uncorrelated by introducing an uncorrelated constraint. Thus the interference from redundant information are reduced. The experimental results from millimeter wave radar target recognition show that UDNPP algorithm can give competitive results in comparison with current popular algorithms.
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