- 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
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