Book contents
- Frontmatter
- Contents
- Preface
- Notation Used
- Abbreviations
- 1 Introduction
- 2 Basics
- 3 Probability Distributions
- 4 Statistical Inference
- 5 Linear Regression
- 6 Neural Networks
- 7 Non-linear Optimization
- 8 Learning and Generalization
- 9 Principal Components and Canonical Correlation
- 10 Unsupervised Learning
- 11 Time Series
- 12 Classification
- 13 Kernel Methods
- 14 Decision Trees, Random Forests and Boosting
- 15 Deep Learning
- 16 Forecast Verification and Post-processing
- 17 Merging of Machine Learning and Physics
- Appendices
- References
- Index
10 - Unsupervised Learning
Published online by Cambridge University Press: 23 March 2023
Book contents
- Frontmatter
- Contents
- Preface
- Notation Used
- Abbreviations
- 1 Introduction
- 2 Basics
- 3 Probability Distributions
- 4 Statistical Inference
- 5 Linear Regression
- 6 Neural Networks
- 7 Non-linear Optimization
- 8 Learning and Generalization
- 9 Principal Components and Canonical Correlation
- 10 Unsupervised Learning
- 11 Time Series
- 12 Classification
- 13 Kernel Methods
- 14 Decision Trees, Random Forests and Boosting
- 15 Deep Learning
- 16 Forecast Verification and Post-processing
- 17 Merging of Machine Learning and Physics
- Appendices
- References
- Index
Summary
Under unsupervised learning, clustering or cluster analysis is first studied. Clustering methods are grouped into non-hierarchical (including K-means clustering) and hierarchical clustering. Self-organizing maps can be used as a clustering method or as a discrete non-linear principal component analysis method. Autoencoders are neural network models that can be used for non-linear principal component analysis. Non-linear canonical correlation analysis can also be performed using neural network models.
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- Introduction to Environmental Data Science , pp. 330 - 371Publisher: Cambridge University PressPrint publication year: 2023
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