| 書(shū)目名稱 | Regression and Fitting on Manifold-valued Data | | 編輯 | Ines Adouani,Chafik Samir | | 視頻video | http://file.papertrans.cn/826/825518/825518.mp4 | | 概述 | Covers the topic in a step-by-step manner.Includes simulations for understanding and potential experiments for a wide range of applications.Covers optimization on most used manifolds in machine learni | | 圖書(shū)封面 |  | | 描述 | .This book introduces in a constructive manner a general framework for regression and fitting methods for many applications and tasks involving data on manifolds. The methodology has important and varied applications in machine learning, medicine, robotics, biology, computer vision, human biometrics, nanomanufacturing, signal processing, and image analysis, etc...The first chapter gives? motivation examples, a wide range of applications, raised challenges,? raised challenges, and some concerns.? The second chapter gives a comprehensive exploration and step-by-step illustrations for Euclidean cases. Another dedicated chapter covers? the geometric tools needed for each manifold and provides expressions and key notions for any application for manifold-valued data.?..All loss functions and optimization methods are given as algorithms and can be easily implemented. In particular, many popular manifolds are considered with? derived and specific formulations. The same philosophy is used in all chapters and all novelties are illustrated with intuitive examples. Additionally, each chapter includes simulations and experiments? on real-world problems for understanding and potential extensions | | 出版日期 | Textbook 2024 | | 關(guān)鍵詞 | manifolds; spline; fitting; regression; optimization | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-61712-6 | | isbn_softcover | 978-3-031-61714-0 | | isbn_ebook | 978-3-031-61712-6 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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