Description: Generalized Principal Component Analysis Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Rene Vidal, Yi Ma, Shankar Sastry Format: Paperback Publisher: Springer-Verlag New York Inc., United States Imprint: Springer-Verlag New York Inc. ISBN-13: 9781493979127, 978-1493979127 Synopsis 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. Rene 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.
Price: 52.6 GBP
Location: Aldershot
End Time: 2025-01-08T09:12:50.000Z
Shipping Cost: 29.12 GBP
Product Images
Item Specifics
Return postage will be paid by: Buyer
Returns Accepted: Returns Accepted
After receiving the item, your buyer should cancel the purchase within: 60 days
Return policy details:
Book Title: Generalized Principal Component Analysis
Number of Pages: 566 Pages
Publication Name: Generalized Principal Component Analysis
Language: English
Publisher: Springer-Verlag New York Inc.
Item Height: 235 mm
Subject: Engineering & Technology, Computer Science, Mathematics
Publication Year: 2018
Type: Textbook
Item Weight: 908 g
Author: Shankar Sastry, Rene Vidal, Yi Ma
Item Width: 155 mm
Series: Interdisciplinary Applied Mathematics
Format: Paperback
