Incremental kernel spectral clustering for online learning of non-stationary data

NEUROCOMPUTING

卷:139

Article

Langone, Rocco¹  Agudelo, Oscar Mauricio¹  De Moor, Bart¹  Suykens, Johan A. K.¹ 

[1] Katholieke Univ Leuven, STADIUS iMinds Future Hlth Dept, Dept Elect Engn ESAT, B-3001 Louvain, Belgium

关键词: Incremental kernel spectral clustering;    Out-of-sample eigenvectors;    LS-SVMs;    Online clustering;    Non-stationary data;    PM10 concentrations;   

DOI  :  10.1016/j.neucom.2014.02.036

来源: Elsevier

【 摘 要 】

In this work a new model for online clustering named Incremental kernel spectral clustering (IKSC) is presented. It is based on kernel spectral clustering (KSC), a model designed in the Least Squares Support Vector Machines (LS-SVMs) framework, with primal-dual setting. The IKSC model is developed to quickly adapt itself to a changing environment, in order to learn evolving clusters with high accuracy. In contrast with other existing incremental spectral clustering approaches, the eigen-updating is performed in a model-based manner, by exploiting one of the Karush-Kuhn-Tucker (KKT) optimality conditions of the KSC problem. We test the capacities of IKSC with some experiments conducted on computer-generated data and a real-world data-set of PM10 concentrations registered during a pollution episode occurred in Northern Europe in January 2010. We observe that our model is able to precisely recognize the dynamics of shifting patterns in a non-stationary context. (C) 2014 Elsevier B.V. All rights reserved.

【 授权许可】

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