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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 894246, 5 pages
Research Article

A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm

1College of Engineering, Bohai University, Jinzhou 121013, China
2Department of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, Norway
3College of Mathematics and Physics, Bohai University, Jinzhou, China
4New Energy College, Bohai University, Jinzhou, China

Received 23 December 2013; Accepted 31 December 2013; Published 11 February 2014

Academic Editor: Ming Liu

Copyright © 2014 Yuping Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample’s distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed.