Intelligent Feature Selection (with SVM or PCA)

[tremblap]

@tedmoore

I normalized the data before the KMeans and the PCA (and SVM).

This is where my work lies now. I am fascinated by the (huge) impact data pre-processing is having on the quality of anything that follows. Log vs Lin and relative range vs relative noise.

@tedmoore

Perhaps it would be good to test it with a dataset that has labels that are not derived from KMeans as a way of checking if the KMeans labels are influencing the result?

Indeed we have labelled data to do such test - for instance in the papers we have some of the cool @danieleghisi curated stuff from orchidea (Computer Assisted Orchestration - #11 by tremblap) you have in the name some interesting human features that @groma is using to validate. For instance, the violin pitch, its timbre-via-technique, and its loudness. This is quite fun