Produktbild: Algorithmic Advances in Riemannian Geometry and Applications

Algorithmic Advances in Riemannian Geometry and Applications

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149,79 €

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For Machine Learning, Computer Vision, Statistics, and Optimization, Advances in Computer Vision and Pattern Recognition

ISBN: 3319450255
ISBN 13: 9783319450254
Herausgeber: Hà Quang Minh/Vittorio Murino
Verlag: Springer Verlag GmbH
Umfang: xiv, 208 S., 4 s/w Illustr., 51 farbige Illustr., 208 p. 55 illus., 51 illus. in color.
Erscheinungsdatum: 21.10.2016
Auflage: 1/2016
Format: 1.6 x 24.2 x 16.2
Gewicht: 521 g
Produktform: Gebunden/Hardback
Einband: Gebunden

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.

Artikelnummer: 9653473 Kategorie:

Beschreibung

This volume presents a comprehensive treatment of Riemannian geometry as a mathematical and computational framework for many problems in machine learning, statistics, optimization, and computer vision. The chapters in the volume are written by leading experts in the field and showcase the latest advances made recently, both theoretically and algorithmically. Examples include the geometrical foundation of Hamiltionian Monte Carlo, large-scale Riemannian optimization of low-rank matrices for matrix completion, and kernel methods on symmetric positive definite matrices for visual object recognition.

Autorenporträt

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.

Herstellerkennzeichnung:


Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

E-Mail: juergen.hartmann@springer.com

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