Subject: Re: Request for information on ICA-Maximum likelihood approach From: Bryan Paton <bryan.paton@xxxxxxxx> Date: Tue, 13 Apr 2010 08:26:29 +1000 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>Brian Gygi wrote: > Doesn't that kind of negate the whole purpose of ICA? Not quite. PCA enforces the rule that components must contain the maximal amount of variance not accounted for by previous components. Where as ICA returns (depending on the ICA algorithm) maximally temporally (or spatially) independent sources. There is no restriction on how much variance (usually) the components must account for only that they are independent. > It's supposed to > find the dimensions that PCA might miss. But if you do a PCA first, all > you will get out are subsets of the dimensions the PCA finds in the > first place. There are also good reasons why you should run PCA (for big data sets) where the number of channels might approach (or be larger than) the square root of the number of sample points. This has the effect of having a "better" number of data points for the ICA weight matrix: http://sccn.ucsd.edu/pipermail/eeglablist/2006/001481.html http://sccn.ucsd.edu/pipermail/eeglablist/2006/001485.html http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA Although again this might depend on the ICA algorithm being used. Thanks. Bryan. -- Bryan Paton PhD candidate Philosophy Program, SOPHIS / School of Psychology & Psychiatry Building 11, w931 Monash University Clayton, VIC, 3800 Australia +613 990 59166