A selective overview of feature screening for ultrahigh-dimensional data
Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, JingYuan [verfasserIn] Zhong, Wei [verfasserIn] Li, RunZe [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Science in China - Asheville, NC : Science in China Press, 1995, 58(2015), 10 vom: 22. Aug., Seite 1-22 |
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| Übergeordnetes Werk: |
volume:58 ; year:2015 ; number:10 ; day:22 ; month:08 ; pages:1-22 |
| Links: |
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| DOI / URN: |
10.1007/s11425-015-5062-9 |
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| 520 | |a Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. | ||
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10.1007/s11425-015-5062-9 doi (DE-627)SPR019140444 (SPR)s11425-015-5062-9-e DE-627 ger DE-627 rakwb eng 510 530 520 ASE 30.00 bkl 31.00 bkl Liu, JingYuan verfasserin aut A selective overview of feature screening for ultrahigh-dimensional data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. correlation learning (dpeaa)DE-He213 distance correlation (dpeaa)DE-He213 sure independence screening (dpeaa)DE-He213 sure joint screening (dpeaa)DE-He213 sure screening property (dpeaa)DE-He213 ultrahigh-dimensional data (dpeaa)DE-He213 Zhong, Wei verfasserin aut Li, RunZe verfasserin aut Enthalten in Science in China Asheville, NC : Science in China Press, 1995 58(2015), 10 vom: 22. Aug., Seite 1-22 (DE-627)325695059 (DE-600)2038800-7 1862-2763 nnns volume:58 year:2015 number:10 day:22 month:08 pages:1-22 https://dx.doi.org/10.1007/s11425-015-5062-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-AST SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 30.00 ASE 31.00 ASE AR 58 2015 10 22 08 1-22 |
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10.1007/s11425-015-5062-9 doi (DE-627)SPR019140444 (SPR)s11425-015-5062-9-e DE-627 ger DE-627 rakwb eng 510 530 520 ASE 30.00 bkl 31.00 bkl Liu, JingYuan verfasserin aut A selective overview of feature screening for ultrahigh-dimensional data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. correlation learning (dpeaa)DE-He213 distance correlation (dpeaa)DE-He213 sure independence screening (dpeaa)DE-He213 sure joint screening (dpeaa)DE-He213 sure screening property (dpeaa)DE-He213 ultrahigh-dimensional data (dpeaa)DE-He213 Zhong, Wei verfasserin aut Li, RunZe verfasserin aut Enthalten in Science in China Asheville, NC : Science in China Press, 1995 58(2015), 10 vom: 22. Aug., Seite 1-22 (DE-627)325695059 (DE-600)2038800-7 1862-2763 nnns volume:58 year:2015 number:10 day:22 month:08 pages:1-22 https://dx.doi.org/10.1007/s11425-015-5062-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-AST SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 30.00 ASE 31.00 ASE AR 58 2015 10 22 08 1-22 |
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10.1007/s11425-015-5062-9 doi (DE-627)SPR019140444 (SPR)s11425-015-5062-9-e DE-627 ger DE-627 rakwb eng 510 530 520 ASE 30.00 bkl 31.00 bkl Liu, JingYuan verfasserin aut A selective overview of feature screening for ultrahigh-dimensional data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. correlation learning (dpeaa)DE-He213 distance correlation (dpeaa)DE-He213 sure independence screening (dpeaa)DE-He213 sure joint screening (dpeaa)DE-He213 sure screening property (dpeaa)DE-He213 ultrahigh-dimensional data (dpeaa)DE-He213 Zhong, Wei verfasserin aut Li, RunZe verfasserin aut Enthalten in Science in China Asheville, NC : Science in China Press, 1995 58(2015), 10 vom: 22. Aug., Seite 1-22 (DE-627)325695059 (DE-600)2038800-7 1862-2763 nnns volume:58 year:2015 number:10 day:22 month:08 pages:1-22 https://dx.doi.org/10.1007/s11425-015-5062-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-AST SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 30.00 ASE 31.00 ASE AR 58 2015 10 22 08 1-22 |
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10.1007/s11425-015-5062-9 doi (DE-627)SPR019140444 (SPR)s11425-015-5062-9-e DE-627 ger DE-627 rakwb eng 510 530 520 ASE 30.00 bkl 31.00 bkl Liu, JingYuan verfasserin aut A selective overview of feature screening for ultrahigh-dimensional data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. correlation learning (dpeaa)DE-He213 distance correlation (dpeaa)DE-He213 sure independence screening (dpeaa)DE-He213 sure joint screening (dpeaa)DE-He213 sure screening property (dpeaa)DE-He213 ultrahigh-dimensional data (dpeaa)DE-He213 Zhong, Wei verfasserin aut Li, RunZe verfasserin aut Enthalten in Science in China Asheville, NC : Science in China Press, 1995 58(2015), 10 vom: 22. Aug., Seite 1-22 (DE-627)325695059 (DE-600)2038800-7 1862-2763 nnns volume:58 year:2015 number:10 day:22 month:08 pages:1-22 https://dx.doi.org/10.1007/s11425-015-5062-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-AST SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 30.00 ASE 31.00 ASE AR 58 2015 10 22 08 1-22 |
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Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. 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Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. |
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Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. |
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Abstract High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures. |
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7.401784 |