SVD vs PCA: Comparison of Performance in an Imaging Spectrometer

The calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular-value decomposition (SVD) and principal component analysis (PCA) in terms of estimation performance with respect...

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Main Authors: Wilma Oblefias, Maricor Soriano, Caesar Saloma
Format: Article
Language:English
Published: University of the Philippines 2004-12-01
Series:Science Diliman
Subjects:
Online Access:http://journals.upd.edu.ph/index.php/sciencediliman/article/view/111
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author Wilma Oblefias
Maricor Soriano
Caesar Saloma
author_facet Wilma Oblefias
Maricor Soriano
Caesar Saloma
author_sort Wilma Oblefias
collection DOAJ
description The calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular-value decomposition (SVD) and principal component analysis (PCA) in terms of estimation performance with respect to resolution, presence of noise, intensity variation, and quantization error. Results show that SVD is robust in intensity variation while PCA is not. However, PCA performs better with signals of low signal-to-noise ratio. No significant difference is seen between SVD and PCA in terms of resolution and quantization error.
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spelling doaj.art-0c640149533c4160926875db61faf2f32022-12-22T00:44:54ZengUniversity of the PhilippinesScience Diliman0115-78092012-08182004-12-011627478SVD vs PCA: Comparison of Performance in an Imaging SpectrometerWilma OblefiasMaricor SorianoCaesar SalomaThe calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular-value decomposition (SVD) and principal component analysis (PCA) in terms of estimation performance with respect to resolution, presence of noise, intensity variation, and quantization error. Results show that SVD is robust in intensity variation while PCA is not. However, PCA performs better with signals of low signal-to-noise ratio. No significant difference is seen between SVD and PCA in terms of resolution and quantization error.http://journals.upd.edu.ph/index.php/sciencediliman/article/view/111singular-value decompositionSVDprincipal component analysisPCA
spellingShingle Wilma Oblefias
Maricor Soriano
Caesar Saloma
SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
Science Diliman
singular-value decomposition
SVD
principal component analysis
PCA
title SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
title_full SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
title_fullStr SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
title_full_unstemmed SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
title_short SVD vs PCA: Comparison of Performance in an Imaging Spectrometer
title_sort svd vs pca comparison of performance in an imaging spectrometer
topic singular-value decomposition
SVD
principal component analysis
PCA
url http://journals.upd.edu.ph/index.php/sciencediliman/article/view/111
work_keys_str_mv AT wilmaoblefias svdvspcacomparisonofperformanceinanimagingspectrometer
AT maricorsoriano svdvspcacomparisonofperformanceinanimagingspectrometer
AT caesarsaloma svdvspcacomparisonofperformanceinanimagingspectrometer