Highly coherent learned dictionaries can assign different physical meanings to the same sparse representation; you need to account for dictionary uncertainty and check which physical interpretations survive across all calibration-compatible alternatives.
When learning dictionaries from data to represent signals sparsely, the selected atoms may seem physically meaningful but could be arbitrary artifacts of the calibration data—especially when many different dictionaries fit equally well.