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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Elements of Dimensionality Reduction and Manifold Learning

Elements of Dimensionality Reduction and Manifold Learning

Crowley, Mark
Karray, Fakhri
Ghojogh, Benyamin

Hardcover

General Computers

ISBN10: 3031106016
ISBN13: 9783031106019
Publisher: Springer Nature
Published: Feb 3 2023
Pages: 606
Weight: 2.34
Height: 1.38 Width: 6.14 Depth: 9.21
Language: English
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.

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