Analysis of multivariate and high-dimensional data /
By: Koch, Inge
Material type: BookSeries: Cambridge series in statistical and probabilistic mathematics.Publisher: Cambridge : Cambridge University Press, c2014.Description: xxv, 504 p. : ill. ; 27 cm.ISBN: 9780521887939 (hardback)Subject(s): Multivariate analysis | Big dataDDC classification: 519.5/35 Online resources: Location MapItem type | Home library | Call number | Status | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 519.535 KO AN (Browse shelf) | Available | T0024637 |
, Shelving location: Main Collection Close shelf browser
519.535 HA MU Multivariate data analysis / | 519.535 HA MU Multivariate data analysis / | 519.535 JO AP Applied multivariate methods for data analysts / | 519.535 KO AN Analysis of multivariate and high-dimensional data / | 519.535 LA AN Analyzing multivariate data / | 519.535 RE AD Reading and understanding more multivariate statistics / | 519.535 SO MU Multiscale modeling of complex molecular structure and dynamics with MBN Explorer / |
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"--