NDLI: Self-adaptive kernel machine: online clustering in RKHS

Content Provider	IEEE Xplore Digital Library
Author	Boubacar, H.A. Lecoeuche, S. Maouche, S.
Copyright Year	2005
Description	Author affiliation: Lab. Automatique, Univ. des Sci. et Technol. de Lille, Villeneuve d'Ascq, France (Boubacar, H.A.; Lecoeuche, S.; Maouche, S.)
Abstract	This paper presents a new online clustering algorithm (called SAKM) that is developed to learn continuously evolving clusters from non-stationary data. The SAKM algorithm is based on SVM methods with kernel trick in reproducing Hilbert space, and uses a fast incremental learning procedure to take into account model changes over time. Dedicated to online clustering in multi-class environment, the algorithm is based on an unsupervised learning process with self-adaptive abilities. The SAKM learning process is based on a specific kernel-induced similarity measure and is designed in four main stages: Creation with an initialisation procedure, adaptation, fusion and elimination. In addition to its new properties, the SAKM algorithm is attractive to be very computationally efficient and to provide good performances in online applications. After a comparison with NORMA and Gentile' ALMA algorithms, some experiments are presented to illustrate the capacities of our algorithm for online clustering of non-stationary data in multi-class environment.
Starting Page	1977
Ending Page	1982
File Size	1601962
Page Count	6
File Format	PDF
ISBN	0780390482
DOI	10.1109/IJCNN.2005.1556183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Support vector machines Neural networks Support vector machine classification Iterative algorithms Kernel Hilbert space Clustering methods Convergence Unsupervised learning
Content Type	Text
Resource Type	Article

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