Elements of Dimensionality Reduction and Manifold Learning

Table of Contents

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