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008			230202s2023 sz | fo |||| 0|eng d
020			_a9783031106026
024	7		_a10.1007/978-3-031-10602-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA347 .A78 _b2023 EB
100	1		_aGhojogh, Benyamin _eautor _0(orcid)0000-0002-9617-291X _1https://orcid.org/0000-0002-9617-291X _4http://id.loc.gov/vocabulary/relators/aut _9689938
245	1	0	_aElements of Dimensionality Reduction and Manifold Learning _cby Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 1: Introduction -- Part 1: Preliminaries and Background -- Chapter 2: Background on Linear Algebra -- Chapter 3: Background on Kernels -- Chapter 4: Background on Optimization -- Part 2: Spectral dimensionality Reduction -- Chapter 5: Principal Component Analysis -- Chapter 6: Fisher Discriminant Analysis -- Chapter 7: Multidimensional Scaling, Sammon Mapping, and Isomap -- Chapter 8: Locally Linear Embedding -- Chapter 9: Laplacian-based Dimensionality Reduction -- Chapter 10: Unified Spectral Framework and Maximum Variance Unfolding -- Chapter 11: Spectral Metric Learning -- Part 3: Probabilistic Dimensionality Reduction -- Chapter 12: Factor Analysis and Probabilistic Principal Component Analysis -- Chapter 13: Probabilistic Metric Learning -- Chapter 14: Random Projection -- Chapter 15: Sufficient Dimension Reduction and Kernel Dimension Reduction -- Chapter 16: Stochastic Neighbour Embedding -- Chapter 17: Uniform Manifold Approximation and Projection (UMAP) -- Part 4: Neural Network-based Dimensionality Reduction -- Chapter 18: Restricted Boltzmann Machine and Deep Belief Network -- Chapter 19: Deep Metric Learning -- Chapter 20: Variational Autoencoders -- Chapter 21: Adversarial Autoencoders.
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.
988			_aSpringer_Computer_2023
650		7	_2embne _9413115 _aInteligencia artificial
700	1		_9689939 _aCrowley, Mark _eautor
700	1		_9689940 _aKarray, Fakhri _eautor
700	1		_9689941 _aGhodsi, Ali _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-10602-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ean _zSI