Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction

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

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Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction

机译：用于计算降维的判别式的正则化差异准则

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摘要

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.

机译：高光谱数据分类在许多应用中已显示出潜力。但是，大量的光谱带会导致过度拟合。减少光谱带的方法，例如线性判别分析，需要矩阵求逆。我们为线性判别式正则化差异（SLRD）标准方法提出了一种半定规划，该方法不需要矩阵求逆。本文建立了一个分类误差界限，并在六个高光谱数据集上以十种方法提供了实验结果，证明了所提出的SLRD技术的有效性。

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来源
《IEEE Transactions on Aerospace and Electronic Systems》 |2017年第5期|2372-2384|共13页
作者
Alex J. Aved; Erik P. Blasch; Jing Peng;
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作者单位

Air Force Research Laboratory, Rome, NY, USA;

Air Force Research Laboratory, Rome, NY, USA;

Montclair State University, Montclair, NJ, USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Hyperspectral imaging; Government; Optimization; Programming; Feature extraction; Linear discriminant analysis; Robustness;

机译：高光谱成像;政府;优化;编程;特征提取;线性判别分析;稳健性;

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Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction

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