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![Elements of Dimensionality Reduction and Manifold Learning](/uploads/posts/2023-02/1675525670_3841__l_m_nts_of_dim_nsionality_r_duction_and_manifold_l_arning.jpg)
Автор: Benyamin Ghojogh, Mark Crowley, Fakhri Karray
Издательство: Springer
Год: 2023
Страниц: 617
Язык: английский
Формат: pdf (true), epub
Размер: 36.06 MB
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. With the explosion of interest and advances in Machine Learning (ML), there has been a corresponding increased need for educational and reference books to explain various aspects of Machine Learning. However, there has not been a comprehensive text tackling the various methods in dimensionality reduction, Manifold Learning, and feature extraction that integrate with modern Machine Learning theory and practice. 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 highlighted 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.