Abstract: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.
胡正平, 王玲丽. 基于L1范数凸包数据描述的多观测样本分类算法[J]. 电子与信息学报, 2012, 34(1): 194-199.
Hu Zheng-Ping, Wang Ling-Li. The Classification Algorithm of Multiple Observation Samples Based on L1 Norm Convex Hull Data Description. , 2012, 34(1): 194-199.