Relevant pattern selection for subspace learning

In this paper, we propose a scheme to improve the performance of subspace learning by using a pattern (data) selection method as preprocessing. Generally, a training set for subspace learning contains irrelevant or unreliable samples, and removing these samples can improve the learning performance. For this purpose, we use pattern selection preprocessing which discriminates decision boundary/non-boundary patterns by class information and neighborhood property, and removes boundary patterns. Performance improvement by pattern selection is investigated for classification and visual tracking problems, and compared with those of the previous methods.