Elements of Dimensionality Reduction and Manifold Learning
Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and mani...
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| Main Authors: | , , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Edición: | 1st ed. 2023. |
| Subjects: | |
| Acceso en liña: | https://doi.org/10.1007/978-3-031-10602-6 |
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Table of Contents:
- 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.