  A multiple instance dictionary learning method using functions of multiple
instances (DL-FUMI) is proposed to address target detection and two-class
classification problems with inaccurate training labels. Given inaccurate
training labels, DL-FUMI learns a set of target dictionary atoms that describe
the most distinctive and representative features of the true positive class as
well as a set of nontarget dictionary atoms that account for the shared
information found in both the positive and negative instances. Experimental
results show that the estimated target dictionary atoms found by DL-FUMI are
more representative prototypes and identify better discriminative features of
the true positive class than existing methods in the literature. DL-FUMI is
shown to have significantly better performance on several target detection and
classification problems as compared to other multiple instance learning (MIL)
dictionary learning algorithms on a variety of MIL problems.
