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The hardware tests focus on memory and communication contention under loads found in the LES algorithm.
Bandwidth demands from both memory and networking in the benchmark LES algorithm are shown to the primary performance inhibitors.
Details are presented of the particular LES algorithm adopted, together with two benchmark problems (one including swirl) that have been used for code validation purposes.
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Secondly, dimension reduction with LE algorithm is carried out on the polarimetric parameter vector.
Secondly, Laplacian Eigenmap (LE) algorithm is used to reduce the dimension of the 27-dimension polarimetric features.
LE algorithm adopts local nonlinear method, and compared with the linear method, it can better express the real corresponding relation between the data.
What is more, LE algorithm has low computational complexity and is often used to deal with the data of PolSAR image.
The dimension reduction process is shown in Fig. 1. Figure 1a, b shows the raw data and the data after dimension reduction, respectively, by using the LE algorithm.
The objective function of the LE algorithm is to minimize the following cost function, and it can ensure that the adjacent sample points are still neighbors after projection.
d a = 16, p = 7. Figure 7 shows the result of classification by using the majority voting principle in the superpixel blocks, and the LE algorithm is used to reduce dimension.
Low dimension Y which is embedded in high-dimension X space can be found when using the LE algorithm, that is ( Y=left{{y}_1,{y}_2cdots {y}_Nright}in {R}^{D_rtimes N} ).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com