Robust and sparse canonical correlation analysis based L(2,p)-norm

The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature e...
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Gespeichert in:

Autor*in:	Zhong-rong Shi [verfasserIn] Sheng Wang [verfasserIn] Chuan-cai Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2017)
Übergeordnetes Werk:	year:2017

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2016.0296

Katalog-ID:	DOAJ017168074

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520			\|a The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion.
650		4	\|a feature selection
650		4	\|a feature extraction
650		4	\|a paired data
650		4	\|a distance measurement
650		4	\|a robust and sparse CCA
650		4	\|a feature fusion method
650		4	\|a group sparse feature selection
650		4	\|a robust feature extraction
650		4	\|a L(2)-norm distance minimization
650		4	\|a canonical correlation analysis
650		4	\|a objective function
650		4	\|a RSCCA-based L(2,p)-norm
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Sheng Wang \|e verfasserin \|4 aut
700	0		\|a Sheng Wang \|e verfasserin \|4 aut
700	0		\|a Chuan-cai Liu \|e verfasserin \|4 aut
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952			\|j 2017

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allfields	10.1049/joe.2016.0296 doi (DE-627)DOAJ017168074 (DE-599)DOAJd6153909076740c6beb08be5cb2acbfb DE-627 ger DE-627 rakwb eng TA1-2040 Zhong-rong Shi verfasserin aut Robust and sparse canonical correlation analysis based L(2,p)-norm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion. feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm Engineering (General). Civil engineering (General) Sheng Wang verfasserin aut Sheng Wang verfasserin aut Chuan-cai Liu verfasserin aut In The Journal of Engineering Wiley, 2013 (2017) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2017 https://doi.org/10.1049/joe.2016.0296 kostenfrei https://doaj.org/article/d6153909076740c6beb08be5cb2acbfb kostenfrei http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2017
spelling	10.1049/joe.2016.0296 doi (DE-627)DOAJ017168074 (DE-599)DOAJd6153909076740c6beb08be5cb2acbfb DE-627 ger DE-627 rakwb eng TA1-2040 Zhong-rong Shi verfasserin aut Robust and sparse canonical correlation analysis based L(2,p)-norm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion. feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm Engineering (General). Civil engineering (General) Sheng Wang verfasserin aut Sheng Wang verfasserin aut Chuan-cai Liu verfasserin aut In The Journal of Engineering Wiley, 2013 (2017) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2017 https://doi.org/10.1049/joe.2016.0296 kostenfrei https://doaj.org/article/d6153909076740c6beb08be5cb2acbfb kostenfrei http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2017
allfields_unstemmed	10.1049/joe.2016.0296 doi (DE-627)DOAJ017168074 (DE-599)DOAJd6153909076740c6beb08be5cb2acbfb DE-627 ger DE-627 rakwb eng TA1-2040 Zhong-rong Shi verfasserin aut Robust and sparse canonical correlation analysis based L(2,p)-norm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion. feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm Engineering (General). Civil engineering (General) Sheng Wang verfasserin aut Sheng Wang verfasserin aut Chuan-cai Liu verfasserin aut In The Journal of Engineering Wiley, 2013 (2017) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2017 https://doi.org/10.1049/joe.2016.0296 kostenfrei https://doaj.org/article/d6153909076740c6beb08be5cb2acbfb kostenfrei http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2017
allfieldsGer	10.1049/joe.2016.0296 doi (DE-627)DOAJ017168074 (DE-599)DOAJd6153909076740c6beb08be5cb2acbfb DE-627 ger DE-627 rakwb eng TA1-2040 Zhong-rong Shi verfasserin aut Robust and sparse canonical correlation analysis based L(2,p)-norm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion. feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm Engineering (General). Civil engineering (General) Sheng Wang verfasserin aut Sheng Wang verfasserin aut Chuan-cai Liu verfasserin aut In The Journal of Engineering Wiley, 2013 (2017) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2017 https://doi.org/10.1049/joe.2016.0296 kostenfrei https://doaj.org/article/d6153909076740c6beb08be5cb2acbfb kostenfrei http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2017
allfieldsSound	10.1049/joe.2016.0296 doi (DE-627)DOAJ017168074 (DE-599)DOAJd6153909076740c6beb08be5cb2acbfb DE-627 ger DE-627 rakwb eng TA1-2040 Zhong-rong Shi verfasserin aut Robust and sparse canonical correlation analysis based L(2,p)-norm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion. feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm Engineering (General). Civil engineering (General) Sheng Wang verfasserin aut Sheng Wang verfasserin aut Chuan-cai Liu verfasserin aut In The Journal of Engineering Wiley, 2013 (2017) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2017 https://doi.org/10.1049/joe.2016.0296 kostenfrei https://doaj.org/article/d6153909076740c6beb08be5cb2acbfb kostenfrei http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2017
language	English
source	In The Journal of Engineering (2017) year:2017
sourceStr	In The Journal of Engineering (2017) year:2017
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	The Journal of Engineering
authorswithroles_txt_mv	Zhong-rong Shi @@aut@@ Sheng Wang @@aut@@ Chuan-cai Liu @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ017168074
language_de	englisch
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callnumber-first	T - Technology
author	Zhong-rong Shi
spellingShingle	Zhong-rong Shi misc TA1-2040 misc feature selection misc feature extraction misc paired data misc distance measurement misc robust and sparse CCA misc feature fusion method misc group sparse feature selection misc robust feature extraction misc L(2)-norm distance minimization misc canonical correlation analysis misc objective function misc RSCCA-based L(2,p)-norm misc Engineering (General). Civil engineering (General) Robust and sparse canonical correlation analysis based L(2,p)-norm
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topic_title	TA1-2040 Robust and sparse canonical correlation analysis based L(2,p)-norm feature selection feature extraction paired data distance measurement robust and sparse CCA feature fusion method group sparse feature selection robust feature extraction L(2)-norm distance minimization canonical correlation analysis objective function RSCCA-based L(2,p)-norm
topic	misc TA1-2040 misc feature selection misc feature extraction misc paired data misc distance measurement misc robust and sparse CCA misc feature fusion method misc group sparse feature selection misc robust feature extraction misc L(2)-norm distance minimization misc canonical correlation analysis misc objective function misc RSCCA-based L(2,p)-norm misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc feature selection misc feature extraction misc paired data misc distance measurement misc robust and sparse CCA misc feature fusion method misc group sparse feature selection misc robust feature extraction misc L(2)-norm distance minimization misc canonical correlation analysis misc objective function misc RSCCA-based L(2,p)-norm misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc feature selection misc feature extraction misc paired data misc distance measurement misc robust and sparse CCA misc feature fusion method misc group sparse feature selection misc robust feature extraction misc L(2)-norm distance minimization misc canonical correlation analysis misc objective function misc RSCCA-based L(2,p)-norm misc Engineering (General). Civil engineering (General)
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title	Robust and sparse canonical correlation analysis based L(2,p)-norm
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title_full	Robust and sparse canonical correlation analysis based L(2,p)-norm
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title_sort	robust and sparse canonical correlation analysis based l(2,p)-norm
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title_auth	Robust and sparse canonical correlation analysis based L(2,p)-norm
abstract	The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion.
abstractGer	The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion.
abstract_unstemmed	The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion.
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title_short	Robust and sparse canonical correlation analysis based L(2,p)-norm
url	https://doi.org/10.1049/joe.2016.0296 https://doaj.org/article/d6153909076740c6beb08be5cb2acbfb http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296 https://doaj.org/toc/2051-3305
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up_date	2024-07-04T00:35:06.171Z
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Robust and sparse canonical correlation analysis based L(2,p)-norm

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