Research On Classification Algorithm With Non-Training Pattern Reject Option In High-Dimensional Space

In the conventional classification problems, a typical assumption made during the design phase is that a new test object always belongs to one of a set of known classes. However, in many practical applications, outliers may appear that were not present during the training, which leads to wrong recognition results. Thus, it makes good sense to design a classification model with reject option.The classification problem with reject option usually assumed that no outlier samples are available in the training process. The reason for this assumption is that outliers may occur occasionally or their measurements might be very costly. In this case, finding an appropriate covering model for training class in high-dimensional space based on the complex geometric distribution of samples is the key problem of the above system. Then one point can be classified correctly by determining whether it is in the coverage area. Based on the idea, some novel classification models with reject option are presented in this paper.In order to combining"matter description"with"matter separation"in classification model design, a nearest neighbor classifier with reject option based on structural risk minimization self-organization multiple region covering model is presented in this paper. The algorithm construct a recognization based self-organization multiple region covering model for training data to reject outlier classes, according to the structural minimization principle. Then, the k-NN distinguish is as a following step to identified the exact class for accepted pattern. Experimental results demonstrate the effectiveness of the classifier.According to the assumption that the samples in each class can be supposed to distribute on a nonlinear manifold, a novel classifier with reject option based on manifold subspace covering model is constructed in this paper. Firstly, a compact coverage is built for the training samples by searching a collection of local linear models, each depicted by a subspace, on nonlinear manifold to describe the training class. Then, the SRC (Sparse Representation Classifier) is used for classification. The experiments show good performance of this method.In order to constuct a more compact coverage model by strengthening the discriminate description between training samples, a classifier with reject option based on minimum L1-ball covering model and discrimination feature description is proposed in this paper, which replaces L2 norm of hyperspherical covering algorithm with L1 norm. The algorithm extracts the discrimination projection feature of training samples by L1-norm maximization principal component analysis. Then, the minimum L1-ball covering model in feature space is constructed, which could improve the performance of a classifier.For small sample size problem, conventional classifiers with reject option based on statistical model could not construct appropriate covering decision boundary on data description. In this case, a novel minimum spanning tree (MST) covering model based classifier with reject option is proposed in this paper according to the data distribution in high-dimensional space. The algorithm describes the target class using MST with the assumption that the edges of the graph are also basic elements of the classifier which offers additional virtual training data for a better coverage. Furthermore, in order to reduce the degradation of the rejection performance due to the existence of unreasonable additional virtual training data, an adjustable coverage radius strategy is presented in coverage construction.