Elements of Dimensionality Reduction and Manifold Learning (Paperback, 2023)

Chapter 1: Introduction- Part 1: Preliminaries and Background- Chapter 2: Background on Linear Algebra- Chapter 3: Background on Kernels- Chapter 4: Background on Optimization- Part 2: Spectral dimensionality Reduction- Chapter 5: Principal Component Analysis- Chapter 6: Fisher Discriminant Analysis- Chapter 7: Multidimensional Scaling, Sammon Mapping, and Isomap- Chapter 8: Locally Linear Embedding- Chapter 9: Laplacian-based Dimensionality Reduction- Chapter 10: Unified Spectral Framework and Maximum Variance Unfolding- Chapter 11: Spectral Metric Learning- Part 3: Probabilistic Dimensionality Reduction- Chapter 12: Factor Analysis and Probabilistic Principal Component Analysis- Chapter 13: Probabilistic Metric Learning- Chapter 14: Random Projection- Chapter 15: Sufficient Dimension Reduction and Kernel Dimension Reduction- Chapter 16: Stochastic Neighbour Embedding- Chapter 17: Uniform Manifold Approximation and Projection (UMAP)- Part 4: Neural Network-based Dimensionality Reduction- Chapter 18: Restricted Boltzmann Machine and Deep Belief Network- Chapter 19: Deep Metric Learning- Chapter 20: Variational Autoencoders- Chapter 21: Adversarial Autoencoders