Table of contents for Matrix methods in data mining and pattern recognition / Lars Elden.


Bibliographic record and links to related information available from the Library of Congress catalog


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Preface; Part I. Linear Algebra Concepts and Matrix Decompositions: 1. Vectors and matrices in data mining and pattern recognition; 2. Vectors and matrices; 3. Linear systems and least squares; 4. Orthogonality; 5. QR decomposition; 6. Singular value decomposition; 7. Reduced rank least squares models; 8. Tensor decomposition; 9. Clustering and non-negative matrix factorization; Part II. Data Mining Applications: 10. Classification of handwritten digits; 11. Text mining; 12. Page ranking for a Web search engine; 13. Automatic key word and key sentence extraction; 14. Face recognition using rensor SVD; Part III. Computing the Matrix Decompositions: 15. Computing Eigenvalues and singular values; Bibliography; Index.


Library of Congress subject headings for this publication:
Data mining.
Pattern recognition systems -- Mathematical models.
Algebras, Linear.