000			02796cam a2200301 i 4500
999			_c3680 _d3680
001			17745256
005			20200213100853.0
008			130520s2014 enk b 001 0 eng
010			_a 2013013351
020			_a9780521887939 (hardback)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA278 _b.K5935 2014
082	0	0	_a519.5/35 _223
100	1		_aKoch, Inge, _d1952-
245	1	0	_aAnalysis of multivariate and high-dimensional data / _cInge Koch, University of Adelaide, Australia.
300			_axxv, 504 pages ; _c27 cm.
490	0		_aCambridge series in statistical and probabilistic mathematics
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.
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"--
650		0	_aMultivariate analysis.
650		0	_aBig data.
856	4	2	_3Full-text here _uhttps://www.cambridge.org/core/books/analysis-of-multivariate-and-highdimensional-data/2BF8DE949E18E3A68001976784087816
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK