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Robust methods for data reduction / Alessio Farcomeni and Luca Greco.

By: Contributor(s): Material type: TextTextPublication details: Boca Raton : CRC Press, ©2015.Description: xxvii, 269 p. : illustrations ; 24 cmISBN:
  • 9781466590625
Subject(s): DDC classification:
  • 000SA.13 23 F221
Contents:
Section 1. Dimension reduction -- 1. Introduction and overview -- 2. Multivariate estimation methods -- 3. Principal component analysis -- 4. Sparse Robust PCA -- 5. Canonical correlation analysis -- 6. Factor analysis -- Section 2. Sample reduction -- 7. k-means and model-based clustering -- 8. Robust clustering -- 9. Robust model-based clustering -- 10. Double clustering -- 11. Discriminant analysis.
Summary: 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 by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analy.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.13 F221 (Browse shelf(Opens below)) Available 137121
Total holds: 0

Includes bibliographical references and index.

Section 1. Dimension reduction --
1. Introduction and overview --
2. Multivariate estimation methods --
3. Principal component analysis --
4. Sparse Robust PCA --
5. Canonical correlation analysis --
6. Factor analysis --
Section 2. Sample reduction --
7. k-means and model-based clustering --
8. Robust clustering --
9. Robust model-based clustering --
10. Double clustering --
11. Discriminant analysis.

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 by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analy.

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