To summarize, a similarity measure quantifies the similarity between a pair of examples, relative to other pairs of examples. The table below compares the two types of similarity measures:
|Manually combining feature data.
|Datasets are small and features are easily combined.
|Gain insight into results of similarity calculations, but if feature data changes, then you must update the similarity measure.
|Measuring distance between embeddings generated via a supervised DNN.
|Datasets are large and features are hard to combine.
|No insight into results, but DNN can automatically adapt to changing feature data.