Sea-Land Clutter Classification Based on Graph Spectrum Features

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset...
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Gespeichert in:

Autor*in:	Le Zhang [verfasserIn] Anke Xue [verfasserIn] Xiaodong Zhao [verfasserIn] Shuwen Xu [verfasserIn] Kecheng Mao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	radar clutter classification graph feature extraction support vector machine average amplitude level

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 22, p 4588
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:22, p 4588

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13224588

Katalog-ID:	DOAJ08704207X

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520			\|a In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
650		4	\|a radar clutter classification
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650		4	\|a average amplitude level
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allfields	10.3390/rs13224588 doi (DE-627)DOAJ08704207X (DE-599)DOAJc229db41ccb64d25b7c8298feedcaf10 DE-627 ger DE-627 rakwb eng Le Zhang verfasserin aut Sea-Land Clutter Classification Based on Graph Spectrum Features 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application. radar clutter classification graph feature extraction support vector machine average amplitude level Science Q Anke Xue verfasserin aut Xiaodong Zhao verfasserin aut Shuwen Xu verfasserin aut Kecheng Mao verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 22, p 4588 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:22, p 4588 https://doi.org/10.3390/rs13224588 kostenfrei https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10 kostenfrei https://www.mdpi.com/2072-4292/13/22/4588 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 22, p 4588
spelling	10.3390/rs13224588 doi (DE-627)DOAJ08704207X (DE-599)DOAJc229db41ccb64d25b7c8298feedcaf10 DE-627 ger DE-627 rakwb eng Le Zhang verfasserin aut Sea-Land Clutter Classification Based on Graph Spectrum Features 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application. radar clutter classification graph feature extraction support vector machine average amplitude level Science Q Anke Xue verfasserin aut Xiaodong Zhao verfasserin aut Shuwen Xu verfasserin aut Kecheng Mao verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 22, p 4588 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:22, p 4588 https://doi.org/10.3390/rs13224588 kostenfrei https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10 kostenfrei https://www.mdpi.com/2072-4292/13/22/4588 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 22, p 4588
allfields_unstemmed	10.3390/rs13224588 doi (DE-627)DOAJ08704207X (DE-599)DOAJc229db41ccb64d25b7c8298feedcaf10 DE-627 ger DE-627 rakwb eng Le Zhang verfasserin aut Sea-Land Clutter Classification Based on Graph Spectrum Features 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application. radar clutter classification graph feature extraction support vector machine average amplitude level Science Q Anke Xue verfasserin aut Xiaodong Zhao verfasserin aut Shuwen Xu verfasserin aut Kecheng Mao verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 22, p 4588 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:22, p 4588 https://doi.org/10.3390/rs13224588 kostenfrei https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10 kostenfrei https://www.mdpi.com/2072-4292/13/22/4588 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 22, p 4588
allfieldsGer	10.3390/rs13224588 doi (DE-627)DOAJ08704207X (DE-599)DOAJc229db41ccb64d25b7c8298feedcaf10 DE-627 ger DE-627 rakwb eng Le Zhang verfasserin aut Sea-Land Clutter Classification Based on Graph Spectrum Features 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application. radar clutter classification graph feature extraction support vector machine average amplitude level Science Q Anke Xue verfasserin aut Xiaodong Zhao verfasserin aut Shuwen Xu verfasserin aut Kecheng Mao verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 22, p 4588 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:22, p 4588 https://doi.org/10.3390/rs13224588 kostenfrei https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10 kostenfrei https://www.mdpi.com/2072-4292/13/22/4588 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 22, p 4588
allfieldsSound	10.3390/rs13224588 doi (DE-627)DOAJ08704207X (DE-599)DOAJc229db41ccb64d25b7c8298feedcaf10 DE-627 ger DE-627 rakwb eng Le Zhang verfasserin aut Sea-Land Clutter Classification Based on Graph Spectrum Features 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application. radar clutter classification graph feature extraction support vector machine average amplitude level Science Q Anke Xue verfasserin aut Xiaodong Zhao verfasserin aut Shuwen Xu verfasserin aut Kecheng Mao verfasserin aut In Remote Sensing MDPI AG, 2009 13(2021), 22, p 4588 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:13 year:2021 number:22, p 4588 https://doi.org/10.3390/rs13224588 kostenfrei https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10 kostenfrei https://www.mdpi.com/2072-4292/13/22/4588 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 13 2021 22, p 4588
language	English
source	In Remote Sensing 13(2021), 22, p 4588 volume:13 year:2021 number:22, p 4588
sourceStr	In Remote Sensing 13(2021), 22, p 4588 volume:13 year:2021 number:22, p 4588
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	radar clutter classification graph feature extraction support vector machine average amplitude level Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Le Zhang @@aut@@ Anke Xue @@aut@@ Xiaodong Zhao @@aut@@ Shuwen Xu @@aut@@ Kecheng Mao @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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author	Le Zhang
spellingShingle	Le Zhang misc radar clutter classification misc graph feature extraction misc support vector machine misc average amplitude level misc Science misc Q Sea-Land Clutter Classification Based on Graph Spectrum Features
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topic_title	Sea-Land Clutter Classification Based on Graph Spectrum Features radar clutter classification graph feature extraction support vector machine average amplitude level
topic	misc radar clutter classification misc graph feature extraction misc support vector machine misc average amplitude level misc Science misc Q
topic_unstemmed	misc radar clutter classification misc graph feature extraction misc support vector machine misc average amplitude level misc Science misc Q
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title	Sea-Land Clutter Classification Based on Graph Spectrum Features
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title_sort	sea-land clutter classification based on graph spectrum features
title_auth	Sea-Land Clutter Classification Based on Graph Spectrum Features
abstract	In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
abstractGer	In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
abstract_unstemmed	In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
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title_short	Sea-Land Clutter Classification Based on Graph Spectrum Features
url	https://doi.org/10.3390/rs13224588 https://doaj.org/article/c229db41ccb64d25b7c8298feedcaf10 https://www.mdpi.com/2072-4292/13/22/4588 https://doaj.org/toc/2072-4292
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Sea-Land Clutter Classification Based on Graph Spectrum Features

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