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This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book.
René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book.
René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University.
Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University.
S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Introduces fundamental statistical, geometric and algebraic concepts
Encompasses relevant data clustering and modeling methods in machine learning
Addresses a general class of unsupervised learning problems
Generalizes the theory and methods of principal component anaylsis to the cases when the data can be severely contaminated with errors and outliers as well as when the data may contain more than one low-dimensional subspace
Preface.- Acknowledgments.- Glossary of Notation.- Introduction.- I Modeling Data with Single Subspace.- Principal Component Analysis.- Robust Principal Component Analysis.- Nonlinear and Nonparametric Extensions.- II Modeling Data with Multiple Subspaces.- Algebraic-Geometric Methods.- Statistical Methods.- Spectral Methods.- Sparse and Low-Rank Methods.- III Applications.- Image Representation.- Image Segmentation.- Motion Segmentation.- Hybrid System Identification.- Final Words.- Appendices.- References.- Index.
| Erscheinungsjahr: | 2018 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 600 |
| Reihe: | Interdisciplinary Applied Mathematics |
| Inhalt: |
xxxii
566 S. 38 s/w Illustr. 83 farbige Illustr. 566 p. 121 illus. 83 illus. in color. |
| ISBN-13: | 9781493979127 |
| ISBN-10: | 1493979124 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Vidal, René
Sastry, Shankar Ma, Yi |
| Auflage: | Softcover reprint of the original 1st ed. 2016 |
| Hersteller: |
Springer US
Springer New York Interdisciplinary Applied Mathematics |
| Maße: | 235 x 155 x 33 mm |
| Von/Mit: | René Vidal (u. a.) |
| Erscheinungsdatum: | 14.04.2018 |
| Gewicht: | 0,896 kg |
René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University.
Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University.
S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Introduces fundamental statistical, geometric and algebraic concepts
Encompasses relevant data clustering and modeling methods in machine learning
Addresses a general class of unsupervised learning problems
Generalizes the theory and methods of principal component anaylsis to the cases when the data can be severely contaminated with errors and outliers as well as when the data may contain more than one low-dimensional subspace
Preface.- Acknowledgments.- Glossary of Notation.- Introduction.- I Modeling Data with Single Subspace.- Principal Component Analysis.- Robust Principal Component Analysis.- Nonlinear and Nonparametric Extensions.- II Modeling Data with Multiple Subspaces.- Algebraic-Geometric Methods.- Statistical Methods.- Spectral Methods.- Sparse and Low-Rank Methods.- III Applications.- Image Representation.- Image Segmentation.- Motion Segmentation.- Hybrid System Identification.- Final Words.- Appendices.- References.- Index.
| Erscheinungsjahr: | 2018 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 600 |
| Reihe: | Interdisciplinary Applied Mathematics |
| Inhalt: |
xxxii
566 S. 38 s/w Illustr. 83 farbige Illustr. 566 p. 121 illus. 83 illus. in color. |
| ISBN-13: | 9781493979127 |
| ISBN-10: | 1493979124 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Vidal, René
Sastry, Shankar Ma, Yi |
| Auflage: | Softcover reprint of the original 1st ed. 2016 |
| Hersteller: |
Springer US
Springer New York Interdisciplinary Applied Mathematics |
| Maße: | 235 x 155 x 33 mm |
| Von/Mit: | René Vidal (u. a.) |
| Erscheinungsdatum: | 14.04.2018 |
| Gewicht: | 0,896 kg |
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