Language:
English
日文
簡体中文
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Generalized principal component anal...
~
Ma, Yi.
Generalized principal component analysis[electronic resource] /
Record Type:
Language materials, printed : Monograph/item
[NT 15000414]:
515
Title/Author:
Generalized principal component analysis/ by Rene Vidal, Yi Ma, S. Shankar Sastry.
Author:
Vidal, Rene.
other author:
Ma, Yi.
Published:
New York, NY : : Springer New York :, 2016.
Description:
xxxii, 566 p. : : ill., digital ;; 24 cm.
Contained By:
Springer eBooks
Subject:
Mathematical analysis.
Subject:
Image processing - Mathematics.
Subject:
Big data.
Subject:
Mathematics.
Subject:
Systems Theory, Control.
Subject:
Image Processing and Computer Vision.
Subject:
Signal, Image and Speech Processing.
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Subject:
Algebraic Geometry.
ISBN:
9780387878119
ISBN:
9780387878102
[NT 15000228]:
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.
[NT 15000229]:
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.
Online resource:
http://dx.doi.org/10.1007/978-0-387-87811-9
Generalized principal component analysis[electronic resource] /
Vidal, Rene.
Generalized principal component analysis
[electronic resource] /by Rene Vidal, Yi Ma, S. Shankar Sastry. - New York, NY :Springer New York :2016. - xxxii, 566 p. :ill., digital ;24 cm. - Interdisciplinary applied mathematics,v.400939-6047 ;. - Interdisciplinary applied mathematics ;37..
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.
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.
ISBN: 9780387878119
Standard No.: 10.1007/978-0-387-87811-9doiSubjects--Topical Terms:
227335
Mathematical analysis.
LC Class. No.: QA300
Dewey Class. No.: 515
Generalized principal component analysis[electronic resource] /
LDR
:03161nam a2200337 a 4500
001
447407
003
DE-He213
005
20161012140251.0
006
m d
007
cr nn 008maaau
008
161201s2016 nyu s 0 eng d
020
$a
9780387878119
$q
(electronic bk.)
020
$a
9780387878102
$q
(paper)
024
7
$a
10.1007/978-0-387-87811-9
$2
doi
035
$a
978-0-387-87811-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA300
072
7
$a
GPFC
$2
bicssc
072
7
$a
SCI064000
$2
bisacsh
072
7
$a
TEC004000
$2
bisacsh
082
0 4
$a
515
$2
23
090
$a
QA300
$b
.V649 2016
100
1
$a
Vidal, Rene.
$3
641090
245
1 0
$a
Generalized principal component analysis
$h
[electronic resource] /
$c
by Rene Vidal, Yi Ma, S. Shankar Sastry.
260
$a
New York, NY :
$b
Springer New York :
$b
Imprint: Springer,
$c
2016.
300
$a
xxxii, 566 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Interdisciplinary applied mathematics,
$x
0939-6047 ;
$v
v.40
505
0
$a
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.
520
$a
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.
650
0
$a
Mathematical analysis.
$3
227335
650
0
$a
Image processing
$x
Mathematics.
$3
461281
650
0
$a
Big data.
$3
571002
650
1 4
$a
Mathematics.
$3
172349
650
2 4
$a
Systems Theory, Control.
$3
463973
650
2 4
$a
Image Processing and Computer Vision.
$3
463967
650
2 4
$a
Signal, Image and Speech Processing.
$3
463860
650
2 4
$a
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
464764
650
2 4
$a
Algebraic Geometry.
$3
464922
700
1
$a
Ma, Yi.
$3
641091
700
1
$a
Sastry, S. Shankar.
$3
641092
710
2
$a
SpringerLink (Online service)
$3
463450
773
0
$t
Springer eBooks
830
0
$a
Interdisciplinary applied mathematics ;
$v
37.
$3
465843
856
4 0
$u
http://dx.doi.org/10.1007/978-0-387-87811-9
950
$a
Mathematics and Statistics (Springer-11649)
based on 0 review(s)
Multimedia
Multimedia file
http://dx.doi.org/10.1007/978-0-387-87811-9
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login