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Linear boundary discriminant analysis based on QR decomposition

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Abstract

Linear boundary discriminant analysis (LBDA) shows good feature extraction performance in the classification problem. However, LBDA suffers from small sample size (SSS) problem and the computation time of it increases exponentially for datasets that are not sufficiently large compared with the number of features. To release these problems, we reformulate LBDA using QR decomposition, and this results in both reducing computation time and resolving SSS problem while classification performance is maintained.

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Notes

  1. Other kinds of pattern separation methods can also be used as a data preprocessing for LBDA.

  2. We can control the number of boundary pattern, n (B), by controlling the number of nearest neighbors k. In most experimental results, the best performance of classification is obtained by small number of k.

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Acknowledgments

This work was partly supported by the Global Frontier R&D Program on Human-centered Interaction for Coexistence funded by the National Research Foundation of Korea grant funded by the Korean Government (MEST) (NRF-M1AXA003-2011-0031425).

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Authors and Affiliations

  1. Samsung Electronics Co., Ltd., Suwon, Korea

    Jin Hee Na & Woo-Sung Kang

  2. Korea Institute of Science and Technology, Seoul, Korea

    Myoung Soo Park

  3. Seoul National University, Seoul, Korea

    Jin Young Choi

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  1. Jin Hee Na
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  2. Myoung Soo Park
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  3. Woo-Sung Kang
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  4. Jin Young Choi
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Correspondence to Myoung Soo Park.

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Na, J.H., Park, M.S., Kang, WS. et al. Linear boundary discriminant analysis based on QR decomposition. Pattern Anal Applic 17, 105–112 (2014). https://doi.org/10.1007/s10044-012-0285-7

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  • DOI: https://doi.org/10.1007/s10044-012-0285-7

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