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A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm

Author

Listed:
  • Yuping Qin
  • Hamid Reza Karimi
  • Dan Li
  • Shuxian Lun
  • Aihua Zhang

Abstract

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.

Suggested Citation

  • Yuping Qin & Hamid Reza Karimi & Dan Li & Shuxian Lun & Aihua Zhang, 2014. "A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:894246
    DOI: 10.1155/2014/894246
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    References listed on IDEAS

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    1. Shen Yin & Steven Ding & Adel Abandan Sari & Haiyang Hao, 2013. "Data-driven monitoring for stochastic systems and its application on batch process," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1366-1376.
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