Articles liés à Unsupervised Learning Approaches for Dimensionality...

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization - Couverture rigide

Tripathy, B. K.; Sundareswaran, Anveshrithaa; Ghela, Shrusti

 
9781032041018: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Couverture rigide
ISBN 10 : 1032041013 ISBN 13 : 9781032041018
Editeur : CRC Press, 2021

Synopsis

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data. Underlying mathematical concepts, derivations, and proofs with logical explanations for these algorithms are discussed, including strengths and limitations. The book highlights important use cases of these algorithms and provides examples along with visualizations. Comparative study of the algorithms is presented to give a clear idea on selecting the best suitable algorithm for a given dataset for efficient dimensionality reduction and data visualization.

FEATURES

  • Demonstrates how unsupervised learning approaches can be used for dimensionality reduction

  • Neatly explains algorithms with a focus on the fundamentals and underlying mathematical concepts

  • Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use

  • Provides use cases, illustrative examples, and visualizations of each algorithm

    • Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis

    • This book is aimed at professionals, graduate students, and researchers in Computer Science and Engineering, Data Science, Machine Learning, Computer Vision, Data Mining, Deep Learning, Sensor Data Filtering, Feature Extraction for Control Systems, and Medical Instruments Input Extraction.

    Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

    À propos de l'auteur

    B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela

    Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

    Éditeur
    CRC Press
    Date d'édition
    2021
    Langue
    anglais
    ISBN 10 
    1032041013
    ISBN 13 
    9781032041018
    Reliure
    Relié
    Nombre de pages
    160
    Coordonnées du fabricant
    non disponible
    Personne responsable
    non disponible