Algorithmic Advances in Riemannian Geometry and Applications

For Machine Learning, Computer Vision, Statistics, and Optimization

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Artificial Intelligence, General Computing
Cover of the book Algorithmic Advances in Riemannian Geometry and Applications by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319450261
Publisher: Springer International Publishing Publication: October 5, 2016
Imprint: Springer Language: English
Author:
ISBN: 9783319450261
Publisher: Springer International Publishing
Publication: October 5, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Nonlinear Systems, Vol. 2 by
Cover of the book Synthesis and Characterization of Piezotronic Materials for Application in Strain/Stress Sensing by
Cover of the book New Approaches in Intelligent Control by
Cover of the book Pumps as Turbines by
Cover of the book The Digital Galactic Complex by
Cover of the book Post-Unification Turkish German Cinema by
Cover of the book Saphenous Vein-Sparing Strategies in Chronic Venous Disease by
Cover of the book Managing Risk in Nanotechnology by
Cover of the book Mathematics Achievement of Immigrant Students by
Cover of the book Fuzzy Solution Concepts for Non-cooperative Games by
Cover of the book Operations Research and Enterprise Systems by
Cover of the book Handbook of Practical Fine Needle Aspiration and Small Tissue Biopsies by
Cover of the book Advances in Ergonomics Modeling, Usability & Special Populations by
Cover of the book Progress in Botany Vol. 80 by
Cover of the book The Space Shuttle Program by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy