Minimally-supervised classification using multiple observation sets

This paper discusses building complex classifiers from a single labeled example and vast number of unlabeled obser-vation sets, each derived from observation of a single pro-cess or object. When data can be measured by observation, it is often plentiful and it is often possible to make more than one observation of the state of a process or object. This pa-per discusses how to exploit the variability across such sets of observations of the same object to estimate class labels for unlabeled examples given a minimal number of labeled examples. In contrast to similar semi-supervised classifica-tion procedures that define the likelihood that two observa-tions share a label as a function of the embedded distance between the two observations, this method uses the Naive Bayes estimate of how often the two observations did result from the same observed process. Exploiting this additional source of information in an iterative estimation procedure can generalize complex classification models from single la-beled observations. Some examples involving classification of tracked objects in a low-dimensional feature space given thousands of unlabeled observation sets are used to illus-trate the effectiveness of this method. 1