Semi-Supervised Fisher Linear Discriminant (SFLD)

Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm is evaluated by its ability to generalize, i.e., to extend this mapping accurately to new data that is commonly referred to as the test data. Good generalization depends crucially on the quality of the training set. Because collecting labeled data is laborious, training sets are typically small. Furthermore, it is often difficult to represent all possible observation scenarios during training, so that the statistics of the training set end up differing from those of the test data, a problem known as the sample selection bias. To address sample selection bias, we introduce a Semi-Supervised Fisher Linear Discriminant (SFLD) that utilizes additional, unlabeled data to improve generalization for both small and biased training sets. We characterize the conditions under which SFLD helps, and illustrate its benefits through experiments on digit and car recognition applications.