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Analysis of multivariate and high-di...

Koch, Inge, (1952-)

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Analysis of multivariate and high-dimensional data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analysis of multivariate and high-dimensional data/ by Inge Koch.
作者:	Koch, Inge,
出版者:	Cambridge :Cambridge University Press, : 2014.,
面頁冊數:	xxvi, 504 p. :ill. (some col.), digital ;26 cm.
內容註:	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.
標題:	Multivariate analysis. -
電子資源:	https://doi.org/10.1017/CBO9781139025805
ISBN:	9781139025805

Analysis of multivariate and high-dimensional data
Koch, Inge,1952-

Analysis of multivariate and high-dimensional data[electronic resource] /by Inge Koch. - Cambridge :Cambridge University Press,2014. - xxvi, 504 p. :ill. (some col.), digital ;26 cm. - Cambridge series on statistical and probabilistic mathematics ;32. - Cambridge series on statistical and probabilistic mathematics ;32..

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.

ISBN: 9781139025805Subjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .K5935 2014

Dewey Class. No.: 519.535

Analysis of multivariate and high-dimensional data
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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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