Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction

Alex J. Aved, Erik P. Blasch, Jing Peng

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Hyperspectral data classification has shown potential in many applications. However, a large number of spectral bands cause overfitting. Methods for reducing spectral bands, e.g., linear discriminant analysis, require matrix inversion. We propose a semidefinite programming for linear discriminants regularized difference (SLRD) criterion approach that does not require matrix inversion. The paper establishes a classification error bound and provides experimental results with ten methods over six hyperspectral datasets demonstrating the efficacy of the proposed SLRD technique.

Original languageEnglish
Article number7906579
Pages (from-to)2372-2384
Number of pages13
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume53
Issue number5
DOIs
StatePublished - Oct 2017

Keywords

  • Classification
  • RELIEF
  • dimensionality reduction
  • feature selection
  • hyperspectral imaging

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