Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions · HackerPapers discussions

Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions — mentioned by bee_rider

Tue, 24 Sep 2024 19:47:54 GMT

I guess that is the same Tropp, at the end of the article, as https://arxiv.org/abs/0909.4061 It is a pretty good framework for eigenvalues, as long as your eigenvalues are good (decay nicely). If your eigenvalues don’t decay nicely… find a different problem!

Paper and discussion history. A mention is not an endorsement.

Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions — mentioned by ajtulloch

Mon, 06 Feb 2017 22:32:56 GMT

If anyone is interested, a much faster technique is using the randomized methods from https://arxiv.org/abs/0909.4061 - see e.g. http://scikit-learn.org/stable/modules/generated/sklearn.dec...

Paper and discussion history. A mention is not an endorsement.

Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions — mentioned by jordigh

Tue, 16 Dec 2014 17:00:06 GMT

I wish everyone would stop saying that PCA is about eigenvectors and eigenvalues... it's about singular vectors and singular values. The best way to compute PCA is NOT to compute the eigenvalues of A'*A. There are specialised SVD algorithms that should be used instead: https://en.wikipedia.org/wiki/Singular_value_decomposition#C... Randomised methods have also gotten popular in recent years: https://code.google.com/p/redsvd/ http://arxiv.org/abs/0909.4061 This is one case where the machine learning community takes a numerical analysis idea and by giving it a different name also loses some of…

Paper and discussion history. A mention is not an endorsement.