caa a22 4500 605540470 CHVBK 20210128100911.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00371-013-0915-0 doi (NATIONALLICENCE)springer-10.1007/s00371-013-0915-0 Unsupervised kernel learning for abnormal events detection [Elektronische Daten] [Weiya Ren, Guohui Li, Boliang Sun, Kuihua Huang] In this paper, we propose a method to detect abnormal events using a novel unsupervised kernel learning algorithm. The key of our method is to learn a suitable feature space and the associated kernel function of the training samples. By considering the self-similarity property of training samples, we assume that the training samples will show the distinctly clustering property in the obtained feature space. Non-negative matrix factorization (NMF) is used to learn the feature space, and the support vector data description (SVDD) method is adopted to measure the clustering degree of instances in the feature space. We append the clustering constraints in the process of learning the feature space and use the bases produced by NMF as the projection matrix to construct the kernel function in SVDD. In other words, we incorporate the minimal enclosing sphere constraints within the NMF formulation. In the process of feature space learning, instances in the obtained feature space will be described better and better by an hypersphere. Our algorithm converges to a local optimal solution by applying an alternating optimization approach. Experimental results on three public datasets and the comparison to the state-of-the-art methods show that our method is effective in detecting and locating unknown abnormal behaviors. Springer-Verlag Berlin Heidelberg, 2014 Kernel learning nationallicence One-class learning nationallicence Anomaly detection nationallicence Non-negative matrix factorization nationallicence Support vector data description nationallicence Ren Weiya College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut Li Guohui College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut Sun Boliang College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut Huang Kuihua College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut The Visual Computer Springer Berlin Heidelberg 31/3(2015-03-01), 245-255 0178-2789 31:3<245 2015 31 371 https://doi.org/10.1007/s00371-013-0915-0 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/s00371-013-0915-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ren Weiya College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Li Guohui College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Sun Boliang College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Huang Kuihua College of Information System and Management, National University of Defense Technology, 410072, Changsha, People's Republic of China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/3(2015-03-01), 245-255 0178-2789 31:3<245 2015 31 371