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Online local fisher risk minimization: a new online kernel method for online classification

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Abstract

This study presents a new online kernel algorithm for online classification, called the online local Fisher rick minimization (OLFRM). Motivated by the Fisher criterion, OLFRM stresses the connection between data by introducing a local Fisher criterion, which is represented by two parts: the local Fisher loss function and the local Fisher regularization term. The local Fisher loss function generates a loss when the distance between heterogeneous nearest neighbors is not large enough, whereas the local Fisher regularization term works by minimizing the distance between homogeneous nearest neighbors. To reduce computational complexity and save memory resources, OLFRM is extended to two budgeted OLFRM (BOLFRM) algorithms. One uses a removal method as the budget maintenance strategy, called BOLFRM-R, and the other adopts an approximate projection method as the budget maintenance strategy, named BOLFRM-AP. For both BOLFRM-R and BOLFRM-AP, this study further designs an outlier scheme based on the unique mechanism of the proposed algorithms for selecting pending support vectors. Comprehensive experiments were conducted to compare the performance of the related algorithms on public datasets, and the results demonstrate that BOLFRMs improve in terms of robustness.

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Acknowledgments

We would like to thank the anonymous reviewers and Editor for their valuable comments and suggestions, which have significantly improved this work. This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant Nos. 19KJA550002 and 19KJA610002, by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Changzhi Su, Li Zhang & Lei Zhao

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Correspondence to Li Zhang.

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Su, C., Zhang, L. & Zhao, L. Online local fisher risk minimization: a new online kernel method for online classification. Appl Intell 53, 17662–17678 (2023). https://doi.org/10.1007/s10489-022-04400-8

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