Abstract: A pattern recognition system compares a set of unlabeled images (or other patterns) having a variation of state in a set-by-set comparison with individual data sets of multiple labeled images (or other patterns) also having a variation of state. The individual data sets are each mapped to a point on a parameter space (e.g., a Grassmannian manifold, a Stiefel manifold, a flag manifold, etc.), and the set of unlabeled images is mapped to a point in the same parameter space. If the point associated with the set of unlabeled images satisfies a distance criterion on the parameter space with regard to one of the points on the parameter space, the data set of unlabeled images is assigned to the class attributed to that point.

Abstract: Variations in the states of patterns can be exploited for their discriminatory information and should not be discarded as noise. A pattern recognition system compares a data set of unlabeled patterns having variations of state in a set-by-set comparison with labeled arrays of individual data sets of multiple patterns also having variations of state. The individual data sets are each mapped to a point on a parameter space, and the points of each labeled array define a subset of the parameter space. If the point associated with the data set of unlabeled patterns satisfies a similarity criterion on the parameter space subset of a labeled array, the data set of unlabeled patterns is assigned to the class attributed to that labeled array.

Abstract: Variations in the states of patterns can be exploited for their discriminatory information and should not be discarded as noise. A pattern recognition system compares a data set of unlabeled patterns having variations of state in a set-by-set comparison with labeled arrays of individual data sets of multiple patterns also having variations of state. The individual data sets are each mapped to a point on a parameter space, and the points of each labeled array define a subset of the parameter space. If the point associated with the data set of unlabeled patterns satisfies a similarity criterion on the parameter space subset of a labeled array, the data set of unlabeled patterns is assigned to the class attributed to that labeled array.

Abstract: A pattern recognition system compares a set of unlabeled images (or other patterns) having a variation of state in a set-by-set comparison with individual data sets of multiple labeled images (or other patterns) also having a variation of state. The individual data sets are each mapped to a point on a parameter space (e.g., a Grassmannian manifold, a Stiefel manifold, a flag manifold, etc.), and the set of unlabeled images is mapped to a point in the same parameter space. If the point associated with the set of unlabeled images satisfies a distance criterion on the parameter space with regard to one of the points on the parameter space, the data set of unlabeled images is assigned to the class attributed to that point.