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Incremental maximum margin criterion based on eigenvalue decomposition updating algorithm

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Abstract

Dimensionality reduction has been proven to be a critical data processing step for face recognition. Maximum margin criterion (MMC) is one of the popular supervised dimensionality reduction algorithms. However, the original implementation of MMC is not suitable for incremental learning problem. In this paper, we first propose an eigenvalue decomposition updating algorithm (EVDU) for symmetric matrix. Then, based on our proposed EVDU technique, we propose an incremental MMC (EVDU-IMMC) method which can update the discriminant vectors of MMC when new samples are inserted into the training set. Experiments on ORL and PIE face databases show that the proposed EVDU-IMMC gives the same performance as the batch MMC with much lower computational complexity. The experimental results also show that our proposed EVDU-IMMC gives better performance than other IMMC method in terms of recognition accuracy and computational efficiency.

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Acknowledgments

This research is supported by Anhui Provincial Natural Science Foundation (No. 1308085MF95), the Pre-research Foundation of NSFC of Anhui Polytechnic University (zryy1305), the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education (Grant No. 30920130122005), China Postdoctoral Science Foundation (2013M531251), NSFC of China (Nos. 61231002, 61073137, 61203243), the Natural Science Foundation of the Anhui Higher Education Institutions of China (No. KJ2013B031) and Jiangxi Provincial Natural Science Foundation of China (20122BAB211025).

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  1. School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, Anhui, China

    Gui-Fu Lu & Jian Zou

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  1. Gui-Fu Lu
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  2. Jian Zou
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Correspondence to Gui-Fu Lu.

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Lu, GF., Zou, J. Incremental maximum margin criterion based on eigenvalue decomposition updating algorithm. Machine Vision and Applications 26, 807–817 (2015). https://doi.org/10.1007/s00138-015-0691-0

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