Linear boundary discriminant analysis based on QR decomposition

Linear boundary discriminant analysis (LBDA) shows good feature extraction performance in the classification problem. However, LBDA suffers from small sample size (SSS) problem and the computation time of it increases exponentially for datasets that are not sufficiently large compared with the number of features. To release these problems, we reformulate LBDA using QR decomposition, and this results in both reducing computation time and resolving SSS problem while classification performance is maintained.