Abstract:
Classical classifier assumed that the test samples must be the same class as the trainning, samples, while in some applications such as network security biological ID recognition and medical diagnoses maybe make error judgment because the classical classifier couldn’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 which is a method for pattern feature projection and rejection classification is proposed in this paper 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 constitutes a new differential feature space. Combined with PCA-L1, a new discrimination projection called differential vector PCA-L1 is obtained. Then, minimum L1-ball covering model as the decision boundary around each class is presented on the feature space. Thus the input pattern of no-object classes could be rejected by the first decision boundary description. Finally, if a pattern is accepted by the above step, the recognition result is obtained by the nearest neighbor method. Experiments on the UCI database, the MNIST database of handwritten digitals and the CMU AMP face expression database show 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.