Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization
Author:   Hà Quang Minh ,  Vittorio Murino
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2016
ISBN:  

9783319450254


Pages:   208
Publication Date:   21 October 2016
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization


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Full Product Details

Author:   Hà Quang Minh ,  Vittorio Murino
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2016
Dimensions:   Width: 15.50cm , Height: 1.70cm , Length: 23.50cm
Weight:   5.022kg
ISBN:  

9783319450254


ISBN 10:   3319450255
Pages:   208
Publication Date:   21 October 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Introduction.- Bayesian Statistical Shape Analysis on the Manifold of Diffeomorphisms.- Sampling Constrained Probability Distributions using Spherical Augmentation.- Geometric Optimization in Machine Learning.- Positive Definite Matrices: Data Representation and Applications to Computer Vision.- From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings.- Dictionary Learning on Grassmann Manifolds.- Regression on Lie Groups and its Application to Affine Motion Tracking.- An Elastic Riemannian Framework for Shape Analysis of Curves and Tree-Like Structures.

Reviews

“The book under review consists of eight chapters, each introducing techniques for solving problems on manifolds and illustrating these with examples. … reading this book would add to my collection of tools for working with data on manifolds and expose me to new problems treatable by these tools. … In each case an effort has been made to provide enough of the underlying theory supporting the techniques, with explicit references where the interested reader can go for further details.” (Tim Zajic, IAPR Newsletter, Vol. 39 (3), July, 2017)


The book under review consists of eight chapters, each introducing techniques for solving problems on manifolds and illustrating these with examples. ... reading this book would add to my collection of tools for working with data on manifolds and expose me to new problems treatable by these tools. ... In each case an effort has been made to provide enough of the underlying theory supporting the techniques, with explicit references where the interested reader can go for further details. (Tim Zajic, IAPR Newsletter, Vol. 39 (3), July, 2017)


Author Information

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.

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