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Here is a much better explanation of PCA: https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues h

Here is a much better explanation of PCA: https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues https://stats.stackexchange.com/questions/2691/making-sense-... The key insight that many are missing is that PCA solves a series of optimization problems, namely that reconstructing the data from the first k PCs gives the best k-dimensional approximation in terms of the squared error. Even more, this is equivalent to assuming that the data lives in a k-dimensional subspace and becomes truly high-dimensional because of normally distributed noise that spills into every direction (dimension).

larrydag 4y ago
I really like the way Harrell uses PCA to build regression analysis in Regression Modeling Strategies https://link.springer.com/book/10.1007/978-3-319-19425-7 https://link.springer.com/book/10.1007/978-3-319-19425-7
- rongopo 4y ago
  Indeed cool stuff!
swyx 4y ago
Principal Components is a wonderful concept, together with sister concepts eigenvalues/vectors, and orthogonality. i wish i could force everyone i talk to to internalize these ideas so that I could have more useful discussions with them. that said, yeah not everything is linearly separable
- deleted 4y ago
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