MARC record from Internet Archive

LEADER: 02744cam a2200313 i 4500
001 2013013351
003 DLC
005 20140415075910.0
008 130520s2014 enk b 001 0 eng
010 $a 2013013351
020 $a9780521887939 (hardback)
040 $aDLC$beng$cDLC$erda$dDLC
042 $apcc
050 00 $aQA278$b.K5935 2014
082 00 $a519.5/35$223
100 1 $aKoch, Inge,$d1952-
245 10 $aAnalysis of multivariate and high-dimensional data /$cInge Koch, University of Adelaide, Australia.
264 1 $aCambridge :$bCambridge University Press,$c[2014]
300 $axxv, 504 pages ;$c27 cm.
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
490 0 $aCambridge series in statistical and probabilistic mathematics
520 $a"'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed 'safe operating zone' for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: small classical data; data from medicine, biology, marketing and finance; high-dimensional data from bioinformatics; functional data from proteomics; and simulated data. High-dimension, low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code and problem sets complete the package. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"--$cProvided by publisher.
504 $aIncludes bibliographical references (pages 483-492) and indexes.
505 8 $aMachine 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.
650 0 $aMultivariate analysis.
650 0 $aBig data.
856 42 $3Cover image$uhttp://assets.cambridge.org/97805218/87939/cover/9780521887939.jpg