Initial Matrix contains "1" for known links (0 for unknown and missed entries) in a link prediction system.
Outputs of Matrix Factorization are predicted values for missed entries.
to claculate AUC :
1-Hide 20% of known links (set 20% of entries with '1' to '0' in the matrix )
2- Sort output of Factorization and discard indecies used for train (80% of '1')
3- Set N to number of hided values
4- get N top predicted values and check if they are hided values (class lable set to '1') or not (class lable set to '0')
5- compute AUC using N top predictions
I know 'perfcurve' in matlab computes AUC, but I need to be sure about the above process to provide labeled data for perfcurve.
Any comment is really appreciated.