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. Author manuscript; available in PMC: 2011 May 4.
Published in final edited form as: J Mach Learn Res. 2010 Mar 1;11:2287–2322.

Table 2.

Results of applying Hard-Impute to the Netflix data. The computations were done on a Intel Xeon Linux 3GHz processor; timings are reported based on MAT-LAB implementations of PROPACK and our algorithm. RMSE is the root mean squared error over the probe set. “train error” is the proportion of error on the observed dataset achieved by our estimator relative to the zero estimator.

rank time (hrs) train error RMSE
20 3.3 0.217 0.986
30 5.8 0.203 0.977
40 6.6 0.194 0.965
60 9.7 0.181 0.966