Title

Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction

Document Type

Article

Publication Date

10-1-2017

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.

DOI

10.1109/TAES.2017.2696236

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