000			03388nam a2200289Ia 4500
000			05303nam a22003255i 4500
001			978-3-031-10602-6
003			DE-He213
005			20240319121102.0
007			cr nn 008mamaa
008			230202s2023 sz | s |||| 0|eng d
020			_a9783031106026 _9978-3-031-10602-6
082			_a6.3
100			_aGhojogh, Benyamin. _937595
245			_aElements of Dimensionality Reduction and Manifold Learning _cby Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi. _h[electronic resource] /
250			_a1st ed. 2023.
260			_aCham _bSpringer International Publishing _c2023
300			_aXXVIII, 606 p. 59 illus., 32 illus. in color. _bonline resource.
520			_aDimensionality 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. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader's comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.
650			_aArtificial intelligence. _937596
650			_aArtificial Intelligence. _937597
700			_aCrowley, Mark. _937598
700			_aGhodsi, Ali. _937599
700			_aKarray, Fakhri. _937600
856			_uhttps://doi.org/10.1007/978-3-031-10602-6
942			_cEBK _2ddc
999			_c15726 _d15726