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Title: An improved TA-SVM method without matrix inversion and its fast implementation for nonstationary datasets
Authors: Shi, Y
Chung, FL 
Wang, S
Keywords: Convex quadratic programming
Core vector machine (CVM)
Drift concepts
Large nonstationary datasets
Time complexity
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2015, v. 26, no. 9, 6948375, p. 2005-2018 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2014.2359954
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