Articoli correlati a Unsupervised Learning Approaches for Dimensionality...

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization - Rilegato

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

 
9781032041018: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Rilegato
ISBN 10: 1032041013 ISBN 13: 9781032041018
Casa editrice: CRC Press, 2021

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull'autore

B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
CRC Press
Data di pubblicazione
2021
Lingua
Inglese
ISBN 10 
1032041013
ISBN 13 
9781032041018
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
160
Contatto del produttore
Taylor and Francis, GPSR Taylor and Francis
gpsr@taylorandfrancis.com

Taylor & Francis Verlag GmbH, Kaufingerstraße 24
München
80331
Germania