Abstract
This paper presents a general linear framework and a competitive model for discriminant analysis with partially labeled data. Our method first utilizes the competitive model to find the reliable training samples. Two indices are given to measure the reliability. In the second stage, discriminant vectors are computed by the proposed framework. We show that under different graph models some popular discriminant analysis algorithms are special cases of the proposed framework. Experimental results suggest that our algorithm is effective and can significantly improve the recognition accuracy.
This work is partially supported by National Science Foundation of China under grant No. 60975083 and U0835005.
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Chen, W., Feng, G., Zou, X., Liu, Z. (2012). A Competitive Model for Semi-supervised Discriminant Analysis. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_46
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DOI: https://doi.org/10.1007/978-3-642-35136-5_46
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