Neuware -This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc.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.Ren� 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 600 pp. Englisch.

Bestandsnummer des Verk�ufers 9781493979127

Verk�ufer kontaktieren

Diesen Artikel melden

Bibliografische Details

Titel: Generalized Principal Component Analysis
Verlag: Springer New York, Springer US Apr 2018
Ver�ffentlichungsdatum: 2018
Sprache: Englisch
ISBN-10: 1493979124
ISBN-13: 9781493979127
Bindung: Taschenbuch
Zustand: Neu

�ber diesen Titel

Inhaltsangabe

This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc.

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.

Ren� 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.

�ber die Autorin bzw. den Autor

Ren� 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.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Anbieterinformationen

Impressum Thorsten Retsch Buchversand Mimpf2000 Ober�lschnitz 16 95517 Emtmannsberg Deutschland Telefon: 09209-2023188 Email: mimpf2000@online.de USt-ID-Nr.: DE 235096871 Wir f�hren gebrauchte B�cher aus allen Sparten der Literatur

Zur Homepage des Verk�ufers

Unternehmensdaten des Verk�ufers

buchversandmimpf2000
Germany

Gesch�ftsbedingungen und Versandinformationen

Verkaufsbedingungen

Widerrufsbelehrung/ Muster-Widerrufsformular/
Allgemeine Gesch�ftsbedingungen und Kundeninformationen/ Datenschutzerkl�rung

Widerrufsrecht f�r Verbraucher
(Verbraucher ist jede nat�rliche Person, die ein Rechtsgesch�ft zu Zwecken abschlie�t, die �berwiegend weder ihrer gewerblichen noch ihrer selbstst�ndigen beruflichen T�tigkeit zugerechnet werden kann.)

Widerrufsbelehrung

Widerrufsrecht
Sie haben das Recht, binnen vierzehn Tagen ohne Angabe von Gr�nden diesen Vertrag zu widerrufen.
Die Widerru...

Mehr Information

Versandbedingungen

Soweit in der Artikelbeschreibung keine andere Frist angegeben ist, erfolgt die Lieferung der Ware innerhalb von 3-5 Werktagen nach Vertragsschluss, bei Vorauszahlung erst nach Eingang des vollst�ndigen Kaufpreises und der Versandkosten. Alle Preise inkl. MwSt.

Impressum & Info zum Verk�ufer