SYSTEM AND METHOD FOR CLASSIFYING IMAGES WITH A COMBINATION OF NEAREST-NEIGHBOR-BASED LABEL PROPAGATION AND KERNEL PRINCIPAL COMPONENT ANALYSIS

Described is a system for detecting and classifying new patterns of objects and images for applications where labeled data is scarce. In operation, the system trains a neural network with unlabeled images and extracts features with the neural network from both the unlabeled images and a set of labeled images to generate a feature space. Labels are propagated in the feature space using nearest neighbors, allowing for modeling of a per-class simplified distribution. An object in a new test image can then be classified using reconstruction error based on the per-class simplified distributions.