Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

B. K. Tripathy

ISBN 10: 103204103X / ISBN 13: 9781032041032

Published by CRC Press, 2023

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.FEATURESDemonstrates how unsupervised learning approaches can be used for dimensionality reductionNeatly explains algorithms with a focus on the fundamentals and underlying mathematical conceptsDescribes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for useProvides use cases, illustrative examples, and visualizations of each algorithmHelps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysisThis 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.

Seller Inventory # 9781032041032

Bibliographic Details

• Title: Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
• Publisher: CRC Press
• Publication Date: 2023
• Language: English
• ISBN 10: 103204103X
• ISBN 13: 9781032041032
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 10D millimeters 234 millimeters width by 156 millimeters height by 10 millimeters depth
• Weight: 278 grams

About this title

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.

About the Author

B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela