Abstract: A system and method enable a set of dataset objects that are K-nearest neighbors (K-NN), based on their Minimax distances to a test object, to be identified without computing the all-pair Minimax distances directly. A pairwise distance between the test object and each dataset object is computed. Iteratively, one of the dataset objects is selected to add to a K-NN set until the K-NN set includes a predefined number of nearest neighbors. The selected dataset object at each iteration is the one for which there is no other unselected dataset object which has a smaller pairwise distance to any of a current subset of objects than the selected dataset object. The current subset of objects includes the test object and the dataset objects currently in the K-NN set. After the K-NN set is identified it may be output or used to generate other information, such as a test object label.

Abstract: A system and method enable a set of dataset objects that are K-nearest neighbors (K-NN), based on their Minimax distances to a test object, to be identified without computing the all-pair Minimax distances directly. A pairwise distance between the test object and each dataset object is computed. Iteratively, one of the dataset objects is selected to add to a K-NN set until the K-NN set includes a predefined number of nearest neighbors. The selected dataset object at each iteration is the one for which there is no other unselected dataset object which has a smaller pairwise distance to any of a current subset of objects than the selected dataset object. The current subset of objects includes the test object and the dataset objects currently in the K-NN set. After the K-NN set is identified it may be output or used to generate other information, such as a test object label.