Algorithmic Advances in Riemannian Geometry and Applications For Mac
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
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Hà Quang Minh Vittorio Murino Editors
Algorithmic Advances in Riemannian Geometry and Applications For Machine Learning, Computer Vision, Statistics, and Optimization
Advances in Computer Vision and Pattern Recognition Founding editor Sameer Singh, Rail Vision, Castle Donington, UK Series editor Sing Bing Kang, Microsoft Research, Redmond, WA, USA Advisory Board Horst Bischof, Graz University of Technology, Austria Richard Bowden, University of Surrey, Guildford, UK Sven Dickinson, University of Toronto, ON, Canada Jiaya Jia, The Chinese University of Hong Kong, Hong Kong Kyoung Mu Lee, Seoul National University, South Korea Yoichi Sato, The University of Tokyo, Japan Bernt Schiele, Max Planck Institute for Computer Science, Saarbrücken, Germany Stan Sclaroff, Boston University, MA, USA
More information about this series at http://www.springer.com/series/4205
Hà Quang Minh Vittorio Murino •
Editors
Algorithmic Advances in Riemannian Geometry and Applications For Machine Learning, Computer Vision, Statistics, and Optimization
123
Editors Hà Quang Minh Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy
Vittorio Murino Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy
ISSN 2191-6586 ISSN 2191-6594 (electronic) Advances in Computer Vision and Pattern Recognition ISBN 978-3-319-45025-4 ISBN 978-3-319-45026-1 (eBook) DOI 10.1007/978-3-319-45026-1 Library of Congress Control Number: 2016948260 © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Overview and Goals The theme of this volume is the application of the rich and powerful theories and techniques of Riemannian geometry to the problems in machine learning, statistics, optimizati
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