Examples (and how to generate them) of various conceptually different datasets to throw at PCA to gain better intuition for it
10466
Making sense of principal component analysis, eigenvectors & eigenvalues
111321
When should we discretize/bin continuous independent variables/features and when should not?
69548
Simultaneously fitting multiple datasets
158823
Principal component analysis "backwards": how much variance of the data is explained by a given linear combination of the variables?
108567
Nielsen & Chuang Exercise 2.2 - “Matrix representations: example”
22871
Generating samples from singular Gaussian distribution
116861
Data augmentation techniques for general datasets?
50148
Real-life examples of common distributions
21801
Intuitive examples of importance sampling
25410
What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)?
24750
Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
126886
Do the principal components change if we apply PCA more than once (recursively) on data?
112134
What are principal component scores?
53419
Nice presentation of correlation matrix
55847
PCA demeaning the data
68224
Data Science Project Ideas
129819
Using canonical correlation analysis (CCA) to find matches