Some approaches to optimal cluster labeling with applications to remote sensing

摘要

This paper presents some approaches for the problem of labeling clusters using information from a given set of labeled and unlabeled patterns. Assigning class labels to the clusters is formulated as finding the best label assignment over all possible label assignments with respect to a criterion. Labeling clusters is also viewed as obtaining probabilities of class labels to the clusters with the maximization of the likelihood function and probability of correct labeling as criteria. Closed form solutions are obtained for the probabilities of class labels to the clusters by maximizing a lower bound on the likelihood criterion. Fixed point iteration equations are developed for obtaining probabilities of class labels to the clusters. The problem of obtaining class labels to the clusters is further formulated as that of minimizing the variance of the proportion estimates of the classes that use both the given labeled and unlabeled patterns. Imperfections in the labels of the given labeled set are incorporated into the criteria. Furthermore, the results of application of these techniques in the processing of remotely sensed multispectral scanner imagery data are presented.