Abstract
Band selection plays a critical role in dimensionality reduction (DR) for hyperspectral image (HSI). In view of the research of the DR method based on manifold, we propose an improved version of the original Locality preserving projection (ILPP), a linear band selection method. The article changes the linear projection constraints of the LPP algorithm by embedding a cluster potential matrix into the Laplacian matrix and forming a projection with two-layer linear structure constructed by a certain rule. The idea is to find a new projection that can preserve the local geometry of the data, enhance the proximity of the similar points and increase the class separability between points that are not similar. Results of experiments on two HSIs confirm that ILPP outperforms several traditional alternatives in the performance of dimensionality reduction.
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This work was supported by the National Natural Science Foundation of China (Grant No. 61572381).
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Xiong, J., Ding, S., Li, B. (2017). An Improved Locality Preserving Projection Method for Dimensionality Reduction with Hyperspectral Image. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_34
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DOI: https://doi.org/10.1007/978-3-319-61833-3_34
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