Matrix Methods in Data Mining and Pattern Recognition

Abstract

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.