A Two-Layer Classifier with Rejection Feature based on Discrimination Projection and Minimum L1-ball Covering Model
XU Cheng-qian · Signal Processing · 2011
Classical classifier supposed that the test pattern must be the same class as the trainning pattern,while that maybe make error judgment in some applications such as network security biological ID recognition and medical diagnoses,because the classical classifier cann' t make rejecting judgment for the existing uncooperative exceptional input pattern.A two-layer classifier with rejection feature based on discrimination projection and minimum L1-ball covering model is proposed to solve this problem.Aiming at the problem that one-class classification ignores discrimination between a given set of classes,the differential vector is defined to represent the detail information of each class,which forms into a new differential feature space.Combined with PCA-L1,a new discrimination projection called differential vector PCA-L1 is computed.Then,minimum L1-ball covering model as the decision boundary around each class is constructed.Thus the input pattern of no-object classes could be rejected by the first decision boundary descriptor model.Finally, if a pattern is accepted by the above L1 -ball covering model,the recognition result is judged by the nearest neighbor classification model.Experiments on the UCI database,the MNIST database of handwritten digitals and the CMU AMP face expression database prove that the method proposed in this paper could achieve good recognition and rejection performance,and it could be applicable in many real pattern recognition fields.