Never compute the inverse of a matrix. Use QR or SVD decompositions for least squared error problems or an optimization technique for convex problems.
What you have is a small under-determined system that can be easily handled using a package like R. You don't need to worry about scaling. You need to worry (A LOT) about over-fitting. Normal OLS will fail disastrously on the problem as you state it. It might be possible to use a regularization technique, but with only 10 data points and 200 parameters to fit, you are unlikely to succeed unless you know a LOT about your problem that you can encode as a prior. On Sun, Apr 11, 2010 at 5:32 PM, prasenjit mukherjee <prasen....@gmail.com>wrote: > I am trying to compute regression coefficients, where the dimensions > are ~ 200 and number of points = 10. Basically I need to compute the > beta matrix using OLS ( refer > http://en.wikipedia.org/wiki/Ordinary_least_squares ). The main > bottleneck seems to be computing the inverse of a 200X200 matrix. > > Any pointers/suggestions ? > > -Thanks, > Prasen >
