Online one-class machines based on the coherence criterion

In this paper, we investigate a novel online one-class classification method. We consider a least-squares optimization problem, where the model complexity is controlled by the coherence criterion as a sparsification rule. This criterion is coupled with a simple updating rule for online learning, which yields a low computational demanding algorithm. Experiments conducted on time series illustrate the relevance of our approach to existing methods.

[1]  Pierre Vandergheynst,et al.  On the exponential convergence of matching pursuits in quasi-incoherent dictionaries , 2006, IEEE Transactions on Information Theory.

[2]  Nirvana Meratnia,et al.  Adaptive and Online One-Class Support Vector Machine-Based Outlier Detection Techniques for Wireless Sensor Networks , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[3]  Paul Honeine,et al.  On-line Nonlinear Sparse Approximation of Functions , 2007, 2007 IEEE International Symposium on Information Theory.

[4]  Simon J. Godsill,et al.  Detection of abrupt spectral changes using support vector machines an application to audio signal segmentation , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  S. Muthukrishnan,et al.  Approximation of functions over redundant dictionaries using coherence , 2003, SODA '03.

[6]  Arthur Gretton,et al.  An online support vector machine for abnormal events detection , 2006, Signal Process..

[7]  Paul Honeine,et al.  Online Prediction of Time Series Data With Kernels , 2009, IEEE Transactions on Signal Processing.

[8]  Michael Elad,et al.  A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.

[9]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[10]  Gert Cauwenberghs,et al.  Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.

[11]  Yi-Hung Liu,et al.  Fast Support Vector Data Descriptions for Novelty Detection , 2010, IEEE Transactions on Neural Networks.

[12]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[13]  Defeng Wang,et al.  Structured One-Class Classification , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[15]  Alexander J. Smola,et al.  Online learning with kernels , 2001, IEEE Transactions on Signal Processing.

[16]  David M. J. Tax,et al.  One-class classification , 2001 .

[17]  Young-Sik Choi,et al.  Least squares one-class support vector machine , 2009, Pattern Recognit. Lett..

[18]  Miguel Lázaro-Gredilla,et al.  Adaptive One-Class Support Vector Machine , 2011, IEEE Transactions on Signal Processing.

[19]  Mikhail F. Kanevski,et al.  A Comparison of One-Class Classifiers for Novelty Detection in Forensic Case Data , 2007, IDEAL.