1st Edition
Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization
174 Pages
46 B/W Illustrations
by
CRC Press
174 Pages
46 B/W Illustrations
by
CRC Press
174 Pages
46 B/W Illustrations
by
CRC Press
Also available as eBook on:
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... Read more
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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
Biography
B.K. Tripathy, Anveshrithaa Sundareswaran, Shrusti Ghela
