Multivariate pattern recognition in chemometrics : illustrated by case studies /

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Bibliographic Details
Imprint:Amsterdam ; New York : Elsevier, 1992.
Description:xi, 325 p. : ill. ; 25 cm.
Language:English
Series:Data handling in science and technology v. 9
Subject:Chemistry -- Statistical methods.
Multivariate analysis
Chemistry -- Statistical methods -- Data processing.
Multivariate analysis -- Data processing
Chemistry -- Statistical methods.
Chemistry -- Statistical methods -- Data processing.
Multivariate analysis.
Multivariate analysis -- Data processing.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/1453006
Hidden Bibliographic Details
Other authors / contributors:Brereton, Richard G.
ISBN:0444897836 (hardback : acid-free paper)
0444897844 (paperback)
0444897852 (software suppl.)
0444897860 (paperback and software suppl.)
Notes:Includes bibliographical references and index.
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245 0 0 |a Multivariate pattern recognition in chemometrics :  |b illustrated by case studies /  |c edited by R.G. Brereton. 
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300 |a xi, 325 p. :  |b ill. ;  |c 25 cm. 
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504 |a Includes bibliographical references and index. 
505 0 0 |t Introduction /  |r R.G. Brereton --  |g Ch. 1.  |t Introduction to Multivariate Space /  |r P.J. Lewi --  |g 1.  |t Introduction.  |g 2.  |t Matrices.  |g 3.  |t Multivariate space.  |g 4.  |t Dimension and rank.  |g 5.  |t Matrix product.  |g 6.  |t Vectors as one-dimensional matrices.  |g 7.  |t Unit matrix as a frame of multivariate space.  |g 8.  |t Product of a matrix with a vector. Projection of points upon a single axis.  |g 9.  |t Multiple linear regression (MLR) as a projection of points upon an axis.  |g 10.  |t Linear discriminant analysis (LDA) as a projection of points on an axis.  |g 11.  |t Product of a matrix with a two-column matrix. Projection of points upon a plane.  |g 12.  |t Product of two matrices as a rotation of points in multivariate space.  |g 13.  |t Factor rotation.  |g 14.  |t Factor data analysis.  |g 15.  |t References --  |g Ch. 2.  |t Multivariate Data Display /  |r P.J. Lewi --  |g 1.  |t Introduction.  |g 2.  |t Basic methods of factor data analysis.  |g 3.  |t Choice of a particular display method.  |g 4.  |t SPECTRAMAP program.  |g 5.  |t The neuroleptics case.  |g 6.  |t Principal components analysis (PCA) with standardization.  |g 7.  |t Principal components analysis (PCA) with logarithms.  |g 8.  |t Correspondence factor analysis (CFA).  |g 9.  |t Spectral map analysis (SMA).  |g 10.  |t References --  |g Ch. 3.  |t Vectors and Matrices : Basic Matrix Algebra /  |r N. Bratchell --  |g 1.  |t Introduction.  |g 2.  |t The data matrix.  |g 3.  |t Vector representation.  |g 4.  |t Vector manipulation.  |g 5.  |t Matrices.  |g 6.  |t Statistical equivalents.  |g 7.  |t References --  |g Ch. 4.  |t The Mathematics of Pattern Recognition /  |r N. Bratchell --  |g 1.  |t Introduction.  |g 2.  |t Rotation and projection.  |g 3.  |t Dimensionality.  |g 4.  |t Expressing the information in the data.  |g 5.  |t Decomposition of data.  |g 6.  |t Final comments.  |g 7.  |t References --  |g Ch. 5.  |t Data Reduction Using Principal Components Analysis /  |r J.M. Deane --  |g 1.  |t Introduction.  |g 2.  |t Principal components analysis.  |g 3.  |t Data reduction by dimensionality reduction.  |g 4.  |t Data reduction by variable reduction.  |g 5.  |t Conclusions --  |g 6.  |t References --  |g Ch. 6.  |t Cluster Analysis /  |r N. Bratchell --  |g 1.  |t Introduction.  |g 2.  |t Two problems.  |g 3.  |t Visual inspection.  |g 4.  |t Measurement of distance and similarity.  |g 5.  |t Hierarchical methods.  |g 6.  |t Optimization partitioning methods.  |g 7.  |t Conclusions --  |g 8.  |t References --  |g Ch. 7.  |t SIMCA - Classification by Means of Disjoint Cross Validated Principal Components Models /  |r O.M. Kvalheim and T.V. Karstang --  |g 1.  |t Introduction.  |g 2.  |t Distance, variance and covariance.  |g 3.  |t The principal component model.  |g 4.  |t Unsupervised principal component modelling.  |g 5.  |t Supervised principal component modelling using cross-validation.  |g 6.  |t Cross validated principal component models.  |g 7.  |t The SIMCA model.  |g 8.  |t Classification of new samples to a class model.  |g 9.  |t Communality and modelling power.  |g 10.  |t Discriminatory ability of variables.  |g 11.  |t Separation between classes.  |g 12.  |t Detection of outliers.  |g 13.  |t Data reduction by means of relevance.  |g 14.  |t Conclusion --  |g 15.  |t Acknowledgements --  |g 16.  |t References --  |g Ch. 8.  |t Hard Modelling in Supervised Pattern Recognition /  |r D. Coomans and D.L. Massart --  |g 1.  |t Introduction.  |g 2.  |t The data set.  |g 3.  |t Geometric representation.  |g 4.  |t Classification rule.  |g 5.  |t Deterministic pattern recognition.  |g 6.  |t Probabilistic pattern recognition.  |g 7.  |t Final remarks --  |g 8.  |t References --  |t Software Appendices --  |t Spectramap /  |r P.J. Lewi.  |g 1.  |t Installation of the program.  |g 2.  |t Execution of the program.  |g 3.  |t Tutorial cases --  |t Sirius /  |r O.M. Kvalheim and T.V. Karstang --  |g 1.  |t Introduction.  |g 2.  |t Starting SIRIUS.  |g 3.  |t The data table.  |g 4.  |t Defining, selecting and storing a class.  |g 5.  |t Principal component modelling.  |g 6.  |t Variance decomposition plots and other graphic representations.  |g 7.  |t Summary --  |g 8.  |t Acknowledgements --  |g 9.  |t References. 
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