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Analysis of multivariate and high-di...

Koch, Inge, (1952-)

Analysis of multivariate and high-dimensional data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Analysis of multivariate and high-dimensional data /Inge Koch.
作者:	Koch, Inge,
出版者:	Cambridge :Cambridge University Press,2014.
面頁冊數:	xxv, 504 p. ;27 cm.
標題:	Big data.
電子資源:	http://assets.cambridge.org/97805218/87939/cover/9780521887939.jpg
ISBN:	9780521887939 (hardback) :

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

Analysis of multivariate and high-dimensional data /Inge Koch. - Cambridge :Cambridge University Press,2014. - xxv, 504 p. ;27 cm. - Cambridge series in statistical and probabilistic mathematics.

Includes bibliographical references (p. 483-492) and indexes.

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 integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed ’safe operating zone’ for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: 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. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"--

ISBN: 9780521887939 (hardback) :NT$2361

LCCN: 2013013351Subjects--Topical Terms:

609582
Big data.

LC Class. No.: QA278 / .K5935 2014

Dewey Class. No.: 519.5/35

Analysis of multivariate and high-dimensional data /
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245 1 0 $a Analysis of multivariate and high-dimensional data / $c Inge Koch.
260 $a Cambridge : $b Cambridge University Press, $c 2014.
300 $a xxv, 504 p. ; $c 27 cm.
490 0 $a Cambridge series in statistical and probabilistic mathematics
504 $a Includes bibliographical references (p. 483-492) and indexes.
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 integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed ’safe operating zone’ for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: 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. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"-- $c Provided by publisher.
650 0 $a Big data. $3 609582
650 0 $a Multivariate analysis. $3 181905
856 4 2 $3 Cover image $u http://assets.cambridge.org/97805218/87939/cover/9780521887939.jpg