Uncertainty Characterization Using Copulas For Classification

We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while allowing the ability to incorporate any desired marginal distributions, i.e., any desired modalities. The proposed model is data driven in that the corresponding copula functions and their parameters are learned from the data. Our results show that the proposed framework can capture the uncertainties more accurately than current state of the practice, and lead to robust and improved classification performance compared to traditional classifiers.