SAKM: Self-adaptive kernel machine A kernel-based algorithm for online clustering

This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account varia...

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Vydáno v:Neural networks Ročník 21; číslo 9; s. 1287 - 1301
Hlavní autoři: Amadou Boubacar, Habiboulaye, Lecoeuche, Stéphane, Maouche, Salah
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.11.2008
Elsevier
Témata:
Algorithms
Applied sciences
Artificial Intelligence
Cluster Analysis
Evolving models
Exact sciences and technology
Flows in networks. Combinatorial problems
Information, signal and communications theory
Models, Statistical
Multi-class problems
Non-stationary data
Online clustering
Online Systems
Operational research and scientific management
Operational research. Management science
Risk
RKHS
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Stochastic Processes
Telecommunications and information theory
Online clustering
RKHS
Non-stationary data
Multi-class problems
Evolving models
Cluster analysis
Automatic classification
On line
Similarity
Adaptability
Support vector machine
Approximation algorithm
Combinatorial optimization
Adaptive method
Hilbert space
Architecture
Theoretical study
Error bound
Non stationary condition
On-line systems
Experimental study
Signal classification
Unsupervised learning
Kernel method
Simulation
Metric
Comparative study
ISSN:0893-6080, 1879-2782
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Shrnutí:This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment.
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ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2008.03.016