1 Introduction

In conclusion to all publications on dimension reduction methods, I am giving an overview of the algorithms discussed here.

2 Roadmap for Dimensionality Reduction

Annotation: The Kernel-PCA is of course not a linear dimension reduction method.

Here are the links to the individual topics.

• PCA in general
• PCA for speed up ML models
• PCA for Visualization
• Randomized PCA
• Incremental PCA
• Kernel PCA
• LDA
• Manifold Learning

3 Conclusion

Now we are finally at the end of the publications on the various topics in the field of data science. I will also give a graphic overview of the Machine Learning areas in a separate post.