Abstract
A novel subspace learning algorithm based on nearest feature line in time-frequency domain is proposed in this paper. The proposed algorithm combines neighborhood discriminant nearest feature line analysis and fractional cosine transform to extract the local discriminant features of the samples. A new discriminant power criterion based on nearest feature line is also presented in this paper. Some experiments are implemented to evaluate the proposed algorithm and the experimental results demonstrate the efficiency of the proposed algorithm.
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Yan, L., Wang, C., Pan, JS. (2012). Nearest Feature Line Discriminant Analysis in DFRCT Domain for Image Feature Extraction. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_27
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DOI: https://doi.org/10.1007/978-3-642-34707-8_27
Publisher Name: Springer, Berlin, Heidelberg
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