Posterior regularization and attribute assessment of under-determined linear mappings

Linear mappings are omnipresent in data processing analysis ranging from regression to distance metric learning. The interpretation of coefficients from under-determined mappings raises an unexpected challenge when the original modeling goal does not impose regularization. Therefore, a general posterior regularization strategy is presented for inducing unique results, and additional sensitivity analysis enables attribute assessment for facilitating model interpretation. An application to infrared spectra reflects data smoothness and indicates improved generalization.