001454539 000__ 05639cam\a2200553\i\4500
001454539 001__ 1454539
001454539 003__ OCoLC
001454539 005__ 20230314003217.0
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001454539 007__ cr\cn\nnnunnun
001454539 008__ 230210s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001454539 019__ $$a1368403210
001454539 020__ $$a9783031106026$$q(electronic bk.)
001454539 020__ $$a3031106024$$q(electronic bk.)
001454539 020__ $$z3031106016
001454539 020__ $$z9783031106019
001454539 0247_ $$a10.1007/978-3-031-10602-6$$2doi
001454539 035__ $$aSP(OCoLC)1369580284
001454539 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX
001454539 049__ $$aISEA
001454539 050_4 $$aTA347.D5
001454539 08204 $$a519.5/36$$223/eng/20230210
001454539 1001_ $$aGhojogh, Benyamin,$$eauthor.
001454539 24510 $$aElements of dimensionality reduction and manifold learning /$$cBenyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi.
001454539 264_1 $$aCham :$$bSpringer,$$c[2023]
001454539 264_4 $$c©2023
001454539 300__ $$a1 online resource (xxviii, 606 pages) :$$billustrations (some color)
001454539 336__ $$atext$$btxt$$2rdacontent
001454539 337__ $$acomputer$$bc$$2rdamedia
001454539 338__ $$aonline resource$$bcr$$2rdacarrier
001454539 504__ $$aIncludes bibliographical references and index.
001454539 5050_ $$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.
001454539 506__ $$aAccess limited to authorized users.
001454539 520__ $$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 readers 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.
001454539 588__ $$aDescription based on print version record.
001454539 650_0 $$aDimension reduction (Statistics)
001454539 650_0 $$aMachine learning$$xMathematics.
001454539 650_0 $$aMachine learning$$xStatistical methods.
001454539 655_0 $$aElectronic books.
001454539 7001_ $$aCrowley, Mark,$$eauthor.
001454539 7001_ $$aKarray, Fakhri,$$eauthor.
001454539 7001_ $$aGhodsi, Ali,$$eauthor.
001454539 77608 $$iPrint version:$$tELEMENTS OF DIMENSIONALITY REDUCTION AND MANIFOLD LEARNING.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2022$$z3031106016$$w(OCoLC)1329419832
001454539 852__ $$bebk
001454539 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-10602-6$$zOnline Access$$91397441.1
001454539 909CO $$ooai:library.usi.edu:1454539$$pGLOBAL_SET
001454539 980__ $$aBIB
001454539 980__ $$aEBOOK
001454539 982__ $$aEbook
001454539 983__ $$aOnline
001454539 994__ $$a92$$bISE