Entry Vidal:2015:GPC from intstatrev.bib

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BibTeX entry

@Book{Vidal:2015:GPC,
  author =       "Rene Vidal",
  title =        "Generalized Principal Component Analysis",
  publisher =    "Springer Science+Business Media",
  address =      "New York, NY, USA",
  pages =        "????",
  year =         "2015",
  DOI =          "https://doi.org/10.1007/978-0-387-87811-9",
  ISBN =         "0-387-87810-6",
  ISBN-13 =      "978-0-387-87810-2",
  LCCN =         "xxxii + 566",
  bibdate =      "Thu Nov 17 06:26:20 MST 2016",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/intstatrev.bib;
                 z3950.loc.gov:7090/Voyager",
  URL =          "http://link.springer.com/10.1007/978-0-387-87811-9",
  abstract =     "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{\'e} 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.",
  acknowledgement = ack-nhfb,
  tableofcontents = "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",
}

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