The Classification Algorithm of Multiple Observation Samples Based on L1 Norm Convex Hull Data Description

In order to construct a high-dimensional data approximate model in the purpose of the best coverage of the distribution of high-dimensional samples, the classification algorithm of multiple observation samples based on L1 norm convex hull data description is proposed. The convex hull for each class in the train set and multiple observation samples in the test set is constructed as the first step. So the classification of multiple observation samples is transformed to the similarity of convex hulls. If the test convex hull and every train hull are not overlapping, L1 norm distance measure is used to solve the similarity of convex hulls. Otherwise, L1 norm distance measure is used to solve the similarity of reduced convex hulls. Then the nearest neighbor classifier is used to solve the classification of multiple observation samples. Experiments on three types of databases show that the proposed method is valid and efficient.