Fig. 3. Systematic comparison of computational time complexity.

This analysis compared the growth in computational time with an increase in the training sample size across all models and dimension reduction methods in the age and gender-based classification task. a The DL models recorded higher run times but showed a more linearly increasing trend with an increase in training sample size. On the other hand, the SML models presented a quadratic trend (except for the LDA method). As a result, the difference between the recorded run times between the two classes of models decreased as the training sample size increased. b To further validate this trend, we conducted a similar analysis on a metric for relative computational time growth (defined as the computational time for the given training sample size normalized by this metric for the smallest sample size). This analysis suggested a lower asymptotic complexity in the relative run time growth for DL models. Source data are provided as a Source Data file.