Further reading

• A survey of dimensionality reduction techniques: https://arxiv.org/pdf/1403.2877.pdf
• A short tutorial for dimensionality reduction: https://www.math.uwaterloo.ca/~aghodsib/courses/f06stat890/readings/tutorial_stat890.pdf
• Guide to 12 dimensionality reduction techniques (with Python code): https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/
• A geometric and intuitive explanation of the covariance matrix and its relationship with linear transformation, an essential building block for understanding and using PCA and SVD: https://datascienceplus.com/understanding-the-covariance-matrix
• The kernel trick: https://dscm.quora.com/The-Kernel-Trick