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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Format: | Article |
Language: | English |
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University of the Philippines
2004-12-01
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Series: | Science Diliman |
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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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format | Article |
id | doaj.art-0c640149533c4160926875db61faf2f3 |
institution | Directory Open Access Journal |
issn | 0115-7809 2012-0818 |
language | English |
last_indexed | 2024-12-12T00:13:10Z |
publishDate | 2004-12-01 |
publisher | University of the Philippines |
record_format | Article |
series | Science Diliman |
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 |
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