3 ms·
k-means are actually useful for bootstrapping statistical mixtures when you don't have good priors. Just for this convenient use alone I would place them above
by kleene_op 5y ago
k-means are actually useful for bootstrapping statistical mixtures when you don't have good priors.
Just for this convenient use alone I would place them above PCA.
- civilized 5y agoWhat does bootstrapping statistical mixtures mean? Why not just bootstrap in the regular old non-parametric way? Why inject k-means into it?
- kleene_op 5y agoWhen you try to approximate your data with a mixture of distributions, say Gaussians for instance, you have to initialize their parameters with some values before running your EM loop. Finding good ones can be very problematic. The k-means process is a reasonable method to get good enough starting values without having to think/compute too much. https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm https://en.wikipedia.org/wiki/Expectation%E2%80%93maximizati...
- civilized 5y agoOK, so for you, "bootstrapping statistical mixtures" means "warm-starting EM mixture modeling". Personally I never would have guessed that, in retrospect it makes sense... to each their own!