Matrix Methods in Data Mining and Pattern Recognition / Edition 1
by Lars Eldén
Shows how modern matrix methods can be applied in data mining and pattern recognition.See more details below
Overview
Shows how modern matrix methods can be applied in data mining and pattern recognition.
Product Details
- ISBN-13:
- 9780898716269
- Publisher:
- SIAM
- Publication date:
- 04/09/2007
- Series:
- Fundamentals of Algorithms Ser., #4
- Edition description:
- New Edition
- Pages:
- 224
- Sales rank:
- 1,241,965
- Product dimensions:
- 6.85(w) x 9.72(h) x 0.63(d)
Table of Contents
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.
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