Hi folks, I'm trying to use mahout's PCA implementation based on SSVD in our application. I understand that in order to avoid densifying the sparse input, mahout provides an option that the mean of cols can be a parameters to pass into the algorithms. However, a lot of time, the scale of each axis is different, is there any way to pass the variance into the algorithms without generating the new data set?
ie, x' = (x-u)/\sigma Also, our original data set maybe in CSV format, and we have cleanup method which can generate clean data set in the mapper. My initial implementation will be that we'll generate an intermediate result in mahout row matrix format, and then pass it to SSVD. However, it will be nice and can save lots of storage if we can do this step on-the-fly when we run the algorithms. Could you give me some feedback about this? Thank you very much. Have a good day. Sincerely, DB Tsai ----------------------------------- Web: http://www.dbtsai.com
