Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)
Michael Fink
We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available examples of related classes. Learning to accurately classify objects from a single training example is often un- feasible due to overfitting effects. However, if the instance representa- tion provides that the distance between each two instances of the same class is smaller than the distance between any two instances from dif- ferent classes, then a nearest neighbor classifier could achieve perfect performance with a single training example. We therefore suggest a two stage strategy. First, learn a metric over the instances that achieves the distance criterion mentioned above, from available examples of other related classes. Then, using the single examples, define a nearest neigh- bor classifier where distance is evaluated by the learned class relevance metric. Finding a metric that emphasizes the relevant dimensions for classification might not be possible when restricted to linear projections. We therefore make use of a kernel based metric learning algorithm. Our setting encodes object instances as sets of locality based descriptors and adopts an appropriate image kernel for the class relevance metric learn- ing. The proposed framework for learning from a single example is demonstrated in a synthetic setting and on a character classification task.