Abstract
A novel subspace learning algorithm based on nearest feature line and directional derivative gradient is proposed in this paper. The proposed algorithm combines neighborhood discriminant nearest feature line analysis and directional derivative gradient to extract the local discriminant features of the samples. A discriminant power criterion based on nearest feature line is used to find the most discriminant direction in this paper. Some experiments are implemented to evaluate the proposed algorithm and the experimental results demonstrate the effectiveness of the proposed algorithm.
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Yan, L., Pan, JS., Zhu, X. (2014). Directional Discriminant Analysis for Image Feature Extraction. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_37
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DOI: https://doi.org/10.1007/978-3-319-01796-9_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
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