B. K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book describes algorithms like Locally Linear Embedding, Laplacian eigenmaps, Semidefinite Embedding, t-SNE to resolve the problem of dimensionality reduction in case of non-linear relationships within the data. Underlying mathematical concepts, derivations, proofs, strengths and limitations of these algorithms are discussed as well.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 174
• Erscheinungstermin: 1. September 2021
• Englisch
• ISBN-13: 9781000438314
• Artikelnr.: 62237878

Autorenporträt

B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela

Inhaltsangabe

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