Robust methods for data reduction

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Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis. The first part of the book illustrates how dimension reduction techniques synthesize available information…

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Title Statements

Full title :Robust methods for data reduction / Alessio Farcomeni, Luca Greco.

Summary

Edition Statements

Publication Date:©20

Publication Statements

Publisher(s):CRC Press LLC
Place of Publication:Boca Raton, FL
Language:eng
Publication Date:©20

The Physical Object

Description:1 online resource

Edition Identifiers

ISBN:9781466590632, 1466590637, 0429167962, 9780429167966
oclc:ocn907924079
LC Classification:TJ217.2
Dewey Decimal:629.8

Front Cover; Dedication; Contents; Preface; Authors; List of Figures; List of Tables; List of Examples and R illustrations; Symbol Description; 1. Introduction and Overview; 2. Multivariate Estimation Methods; Section I: Dimension Reduction; Introduction to Dimension Reduction; 3. Principal Component Analysis; 4. Sparse Robust PCA; 5. Canonical Correlation Analysis; 6. Factor Analysis; Section II: Sample Reduction; Introduction to Sample Reduction; 7. k-means and Model-Based Clustering; 8. Robust Clustering; 9. Robust Model-Based Clustering; 10. Double Clustering; 11. Discriminant Analysis.

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