MARC details
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03388nam a2200289Ia 4500 |
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05303nam a22003255i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
978-3-031-10602-6 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240319121102.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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230202s2023 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031106026 |
| -- |
978-3-031-10602-6 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
6.3 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ghojogh, Benyamin. |
| 9 (RLIN) |
37595 |
| 245 ## - TITLE STATEMENT |
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| Title |
Elements of Dimensionality Reduction and Manifold Learning |
| Statement of responsibility, etc. |
by Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi. |
| Medium |
[electronic resource] / |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
1st ed. 2023. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Cham |
| Name of publisher, distributor, etc. |
Springer International Publishing |
| Date of publication, distribution, etc. |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XXVIII, 606 p. 59 illus., 32 illus. in color. |
| Other physical details |
online resource. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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. 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Artificial intelligence. |
| 9 (RLIN) |
37596 |
|
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| Topical term or geographic name entry element |
Artificial Intelligence. |
| 9 (RLIN) |
37597 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Crowley, Mark. |
| 9 (RLIN) |
37598 |
|
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| Personal name |
Ghodsi, Ali. |
| 9 (RLIN) |
37599 |
|
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| Personal name |
Karray, Fakhri. |
| 9 (RLIN) |
37600 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://doi.org/10.1007/978-3-031-10602-6">https://doi.org/10.1007/978-3-031-10602-6</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
e-Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
