| 书目名称 | Elements of Dimensionality Reduction and Manifold Learning | | 编辑 | Benyamin Ghojogh,Mark Crowley,Ali Ghodsi | | 视频video | | | 概述 | Explains the theory of fundamental algorithms in dimensionality reduction, in a step-by-step and very detailed approach.Useful for anyone who wants to understand the ways to extract, transform, and un | | 图书封面 |  | | 描述 | Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms..The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Acad | | 出版日期 | Textbook 2023 | | 关键词 | Data Reduction; Data Visualization; Dimensionality Reduction; Feature Extraction; Machine Learning; Manif | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-10602-6 | | isbn_softcover | 978-3-031-10604-0 | | isbn_ebook | 978-3-031-10602-6 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 18:08:11
