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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

Name: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
Brand: Taylor & Francis Ltd
SKU: 9781032041018
Price: 1302.78 RON
Availability: InStock

Autor: Tripathy, B.K. (Vellore Institute of Technology, Vellore, India.), S, Anveshrithaa (Vellore Institute of Technology, India.), Ghela, Shrusti (Vellore Institute of Technology, India.)

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Editura: Taylor & Francis Ltd

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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such...

Informații despre carte

Autor

Tripathy, B.K. (Vellore Institute of Technology, Vellore, India.), S, Anveshrithaa (Vellore Institute of Technology, India.), Ghela, Shrusti (Vellore Institute of Technology, India.)

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2021

Pagini

160

EAN

9781032041018

ISBN

1032041013

Enbook ID

35826629

Editura

Taylor & Francis Ltd

Greutate

480

Dimensiuni

156 x 234

Descriere completă

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.

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Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

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