3 ms·
you can look at it in terms of reconstruction error
by autokad 9y ago
you can look at it in terms of reconstruction error
- neumann 9y agoThis is the standard way when looking for reduced order models in fluid mechanics. Variations on this: i) How 'faithfully' does it represent the data, eg, how many modes (components) are needed to resolve accuracy in a particular metric, or the entire system ii) What is the cut-off component number which has a signal of order of the measurement uncertainty.
- JorgeGT 9y agoHe, I just happened to have MATLAB on my other screen performing a PCA of a CFD simulation. I got bored of watching the progress bar and decided to browse HN a bit. I guess I cannot escape...
- alexcnwy 9y agoYeah PCA will give you eigenvalues of the PCs in descending order of variance explained so just summing and weighting those tells you the first 3 PCs explain say 93% of the variance in the data.
- Houshalter 9y agoBut the question is when exactly to stop, because the reconstruction error is always going to get lower. The bayesian solution, IIUC, is something like "stop when information required to store the new PCs is more than the information gained from the reduced reconstruction error." The only issue with this is, if you get tons of data then there will be less uncertainty in the principal components. And so it will recommend as many as possible, even if they only decrease the reconstruction error a tiny bit.