Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- Appendix A High-Dimensional Geometry
- Appendix B Probability Theory
- Appendix C Functional Analysis
- Appendix D Matrix Analysis
- Appendix E Approximation Theory
- References
- Index
Appendix D - Matrix Analysis
from Appendices
Published online by Cambridge University Press: 21 April 2022
- Frontmatter
- Dedication
- Contents
- Preface
- Notation
- Part One Machine Learning
- Part Two Optimal Recovery
- Part Three Compressive Sensing
- Part Four Optimization
- Part Five Neural Networks
- Appendices
- Appendix A High-Dimensional Geometry
- Appendix B Probability Theory
- Appendix C Functional Analysis
- Appendix D Matrix Analysis
- Appendix E Approximation Theory
- References
- Index
Summary
This appendix establishes some crucial results about eigenvalues, singular values, and matrix norms. Of particular importance are the Mirsky inequality and the von Neumann trace inequality.
- Type
- Chapter
- Information
- Mathematical Pictures at a Data Science Exhibition , pp. 285 - 296Publisher: Cambridge University PressPrint publication year: 2022