Algorithmic Advances in Riemannian Geometry and Applications
Language: English
Published by Springer International Publishing, Springer International Publishing Okt 2016, 2016
Series: Book 57 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- New

Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
AbeBooks seller since January 23, 2017
Condition: New
£ 130.86
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Add to basketItem description from seller
This item is printed on demand - Print on Demand Titel. Neuware -This book presents a selection of the most recent algorithmic advances in Riemanniangeometry in the context of machine learning, statistics, optimization, computervision, and related fields. The unifying theme of the different chapters in the bookis the exploitation of the geometry of data using the mathematical machinery ofRiemannian geometry. As demonstrated by all the chapters in the book, when the datais intrinsically non-Euclidean, the utilization of this geometrical information can leadto better algorithms that can capture more accurately the structures inherent in thedata, leading ultimately to better empirical performance. This book is not intended tobe an encyclopedic compilation of the applications of Riemannian geometry. Instead, itfocuses on several important research directions that are currently actively pursued byresearchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionarylearning and sparse coding on manifolds. Examples of applications include novel algorithmsfor Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.…
Seller Inventory # 9783319450254
- Title
- Algorithmic Advances in Riemannian Geometry and Applications
- Author
- Vittorio Murino
- Publisher
- Springer International Publishing, Springer International Publishing Okt 2016
- Publication year
- 2016
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3319450255
- ISBN 13
- 9783319450254
- Item weight
- 547 grams
- Dimensions
- 241x160x17 mm
- Series
- Book 57 of 86: Advances in Computer Vision and Pattern Recognition
This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Hà Quang Minh is a researcher in the Pattern Analysis and Computer Vision (PAVIS) group, at the Italian Institute of Technology (IIT), in Genoa, Italy.
Dr. Vittorio Murino is a full professor at the University of Verona Department of Computer Science, and the Director of the PAVIS group at the IIT."About the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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