Analysis of multivariate and high-dimensional data:
'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical le...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2014
|
| Series: | Cambridge series on statistical and probabilistic mathematics
32 |
| Subjects: | |
| Online Access: | BSB01 FHN01 UER01 Volltext |
| Summary: | 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines |
| Item Description: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Physical Description: | 1 online resource (xxv, 504 pages) |
| ISBN: | 9781139025805 |
| DOI: | 10.1017/CBO9781139025805 |
Staff View
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| 505 | 8 | |a Machine generated contents note: Part I. Classical Methods: 1. Multidimensional data; 2. Principal component analysis; 3. Canonical correlation analysis; 4. Discriminant analysis; Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities; 6. Cluster analysis; 7. Factor analysis; 8. Multidimensional scaling; Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity; 10. Independent component analysis; 11. Projection pursuit; 12. Kernel and more independent component methods; 13. Feature selection and principal component analysis revisited; Index | |
| 520 | |a 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines | ||
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Record in the Search Index
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| author | Koch, Inge |
| author_facet | Koch, Inge |
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| contents | Machine generated contents note: Part I. Classical Methods: 1. Multidimensional data; 2. Principal component analysis; 3. Canonical correlation analysis; 4. Discriminant analysis; Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities; 6. Cluster analysis; 7. Factor analysis; 8. Multidimensional scaling; Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity; 10. Independent component analysis; 11. Projection pursuit; 12. Kernel and more independent component methods; 13. Feature selection and principal component analysis revisited; Index |
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| dewey-full | 519.5/35 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 |
| dewey-search | 519.5/35 |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV043940762 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:14Z |
| institution | BVB |
| isbn | 9781139025805 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029349732 |
| oclc_num | 967600665 |
| open_access_boolean | |
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| owner_facet | DE-12 DE-29 DE-92 |
| physical | 1 online resource (xxv, 504 pages) |
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| publishDate | 2014 |
| publishDateSearch | 2014 |
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| publisher | Cambridge University Press |
| record_format | marc |
| series | Cambridge series on statistical and probabilistic mathematics |
| series2 | Cambridge series on statistical and probabilistic mathematics |
| spelling | Koch, Inge Verfasser aut Analysis of multivariate and high-dimensional data Inge Koch, University of Adelaide, Australia Analysis of Multivariate & High-Dimensional Data Cambridge Cambridge University Press 2014 1 online resource (xxv, 504 pages) txt rdacontent c rdamedia cr rdacarrier Cambridge series on statistical and probabilistic mathematics 32 Title from publisher's bibliographic system (viewed on 05 Oct 2015) Machine generated contents note: Part I. Classical Methods: 1. Multidimensional data; 2. Principal component analysis; 3. Canonical correlation analysis; 4. Discriminant analysis; Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities; 6. Cluster analysis; 7. Factor analysis; 8. Multidimensional scaling; Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity; 10. Independent component analysis; 11. Projection pursuit; 12. Kernel and more independent component methods; 13. Feature selection and principal component analysis revisited; Index 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines Multivariate analysis Big data Hochdimensionale Daten (DE-588)7862975-5 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 s Hochdimensionale Daten (DE-588)7862975-5 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 978-0-521-88793-9 Cambridge series on statistical and probabilistic mathematics 32 (DE-604)BV041460443 32 https://doi.org/10.1017/CBO9781139025805 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Koch, Inge Analysis of multivariate and high-dimensional data Cambridge series on statistical and probabilistic mathematics Machine generated contents note: Part I. Classical Methods: 1. Multidimensional data; 2. Principal component analysis; 3. Canonical correlation analysis; 4. Discriminant analysis; Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities; 6. Cluster analysis; 7. Factor analysis; 8. Multidimensional scaling; Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity; 10. Independent component analysis; 11. Projection pursuit; 12. Kernel and more independent component methods; 13. Feature selection and principal component analysis revisited; Index Multivariate analysis Big data Hochdimensionale Daten (DE-588)7862975-5 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)7862975-5 (DE-588)4040708-1 |
| title | Analysis of multivariate and high-dimensional data |
| title_alt | Analysis of Multivariate & High-Dimensional Data |
| title_auth | Analysis of multivariate and high-dimensional data |
| title_exact_search | Analysis of multivariate and high-dimensional data |
| title_full | Analysis of multivariate and high-dimensional data Inge Koch, University of Adelaide, Australia |
| title_fullStr | Analysis of multivariate and high-dimensional data Inge Koch, University of Adelaide, Australia |
| title_full_unstemmed | Analysis of multivariate and high-dimensional data Inge Koch, University of Adelaide, Australia |
| title_short | Analysis of multivariate and high-dimensional data |
| title_sort | analysis of multivariate and high dimensional data |
| topic | Multivariate analysis Big data Hochdimensionale Daten (DE-588)7862975-5 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Multivariate analysis Big data Hochdimensionale Daten Multivariate Analyse |
| url | https://doi.org/10.1017/CBO9781139025805 |
| volume_link | (DE-604)BV041460443 |
| work_keys_str_mv | AT kochinge analysisofmultivariateandhighdimensionaldata AT kochinge analysisofmultivariatehighdimensionaldata |