caa a22 4500 60547043X CHVBK 20210128100328.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00500-014-1331-8 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1331-8 Privacy preserving and fast decision for novelty detection using support vector data description [Elektronische Daten] [Wenjun Hu, Shitong Wang, Fu-lai Chung, Yong Liu, Wenhao Ying] Support vector data description (SVDD) has been widely used in novelty detection applications. Since the decision function of SVDD is expressed through the support vectors which contain sensitive information, the support vectors will be disclosed when SVDD is used to detect the unknown samples. Accordingly, privacy concerns arise. In addition, when it is applied to large datasets, SVDD does not scale well as its complexity is linear with the size of the training dataset (actually the number of support vectors). Our work here is distinguished in two aspects. First, by decomposing the kernel mapping space into three subspaces and exploring the pre-image of the center of SVDD's sphere in the original space, a fast decision approach of SVDD, called FDA-SVDD, is derived, which includes three implementation versions, called FDA-SVDD-I, FDA-SVDD-II and FDA-SVDD-III. The decision complexity of the proposed method is reduced to only $$O$$ O (1). Second, as the decision function of FDA-SVDD only refers to the pre-image of the sphere center, the privacy of support vectors can be preserved. Therefore, the proposed FDA-SVDD is particularly attractive in privacy-preserving novelty detection applications. Empirical analysis conducted on UCI and USPS datasets demonstrates the effectiveness of the proposed approach and verifies the derived theoretical results. Springer-Verlag Berlin Heidelberg, 2014 Novelty detection nationallicence Support vector data description (SVDD) nationallicence Decision complexity nationallicence Kernelized sphere nationallicence Integrated squared error (ISE) nationallicence Pre-image of sphere center nationallicence Hu Wenjun School of Digital Media, Jiangnan University, 214122, Wuxi, China aut Wang Shitong School of Digital Media, Jiangnan University, 214122, Wuxi, China aut Chung Fu-lai Department of Computing, Hong Kong Polytechnic University, Hong Kong, China aut Liu Yong Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, Zhejiang, China aut Ying Wenhao School of Digital Media, Jiangnan University, 214122, Wuxi, China aut Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1171-1186 1432-7643 19:5<1171 2015 19 500 https://doi.org/10.1007/s00500-014-1331-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1331-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hu Wenjun School of Digital Media, Jiangnan University, 214122, Wuxi, China aut NATIONALLICENCE 700 1- Wang Shitong School of Digital Media, Jiangnan University, 214122, Wuxi, China aut NATIONALLICENCE 700 1- Chung Fu-lai Department of Computing, Hong Kong Polytechnic University, Hong Kong, China aut NATIONALLICENCE 700 1- Liu Yong Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, Zhejiang, China aut NATIONALLICENCE 700 1- Ying Wenhao School of Digital Media, Jiangnan University, 214122, Wuxi, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1171-1186 1432-7643 19:5<1171 2015 19 500