A simple method to improve principal components regression

Stat ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Wenjun Lang ◽  
Hui Zou
Keyword(s):  
Principal Components ◽  
Principal Components Regression ◽  
Simple Method

Deletion Criteria for Principal Components Regression Analysis

1978 ◽  
Vol 60 (3) ◽  
pp. 524-527 ◽  
Author(s):  
Thomas B. Fomby ◽  
R. Carter Hill
Keyword(s):  
Regression Analysis ◽  
Principal Components ◽  
Principal Components Regression

Robust Principal Components Regression

Compstat ◽  
2002 ◽  
pp. 515-520
Author(s):  
Sabine Verboven ◽  
Mia Hubert
Keyword(s):  
Principal Components ◽  
Principal Components Regression

scholarly journals Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band

2013 ◽  
Vol 6 (5) ◽  
pp. 8339-8370 ◽  
Author(s):  
A. Hollstein ◽  
R. Lindstrot
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spectral Resolution ◽  
Principal Components ◽  
Hyperspectral Data ◽  
Simple Method ◽  
Computational Burden ◽  
Versatile Tool ◽  
Fast Reconstruction ◽  
Simulation Results

Abstract. Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top of atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution.

Sign in / Sign up

Export Citation Format

Share Document