Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

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B.K. Tripathy (Vellore Institute of Technology, Vellore, India.) Anveshrithaa Sundareswaran (Vellore Institute of Technology, India.)

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

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

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English

CRC Press
25 September 2023

Probability & statistics; Automatic control engineering; Environmental science, engineering & technology; Databases; Computer science

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.

By: B.K. Tripathy (Vellore Institute of Technology Vellore India.), Anveshrithaa Sundareswaran (Vellore Institute of Technology, India.), Shrusti Ghela (Vellore Institute of Technology, India.)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 322g
ISBN: 9781032041032
ISBN 10: 103204103X
Pages: 160
Publication Date: 25 September 2023
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Chapter 1 Introduction to Dimensionality Reduction Chapter 2 Principal Component Analysis (PCA) Chapter 3 Dual PCA Chapter 4 Kernel PCA Chapter 5 Canonical Correlation Analysis (CCA Chapter 6 Multidimensional Scaling (MDS) Chapter 7 Isomap Chapter 8 Random Projections Chapter 9 Locally Linear Embedding Chapter 10 Spectral Clustering Chapter 11 Laplacian Eigenmap Chapter 12 Maximum Variance Unfolding Chapter 13 t-Distributed Stochastic Neighbor Embedding (t-SNE Chapter 14 Comparative Analysis of Dimensionality Reduction Techniques

B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela