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• The objective is to maximize the learning ability about the target class. • Reduces the number of training samples. • Uses the local geometry of the distribution to estimate the extreme boundary points. • Selects the most promising boundary data points as training samples. The objective of this paper is to design an algorithm to maximize the learning ability and knowledge about the target class while minimizing the number of training samples for support vector data description (SVDD). With this motivation, a novel training sample reduction algorithm is proposed in this paper that selects the most promising boundary data points as training set. The proposed approach uses the local geometry of the distribution to estimate the farthest boundary points (also known as extreme points). The legitimacy of the proposed algorithm is verified via experiments performed on MNIST, Iris, UCI default credit card, svmguide and Indian Pines datasets. [ABSTRACT FROM AUTHOR]