Nonisometric Surface Registration via Conformal Laplace–Beltrami Basis Pursuit

Schonsheck, Stefan C.; Bronstein, Michael M.; Lai, Rongjie

doi:10.1007/s10915-020-01390-y

Cited by 2 publications

(1 citation statement)

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“…Since there are many factors that influence the boarding node of flight, it is necessary to reduce the dimension of the data. Laplacian features are applied to map flight ground attributes and inbound guarantee data for dimensionality reduction [11] . The general steps are as follows:…”

Section: Data Dimensionality Reductionmentioning

confidence: 99%

Dynamic prediction of flight boarding allowed time based on improved SVR

Kan²,

Zhang³

et al. 2022

Second International Symposium on Computer Technology and Information Science (ISCTIS 2022)

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Aiming at the problem of low efficiency in obtaining flight boarding allowed nodes at large busy airports, the randomness and regularity of the flight from the actual time of arrival to the release of flight boarding allowed node is studied. After using Laplace feature map to reduce the data dimensionality, a prediction model of flight boarding allowed node is constructed based on support vector regression. The model analyzes and extracts the main factors that have an impact on the nodes that boarding allowed, and groups the daily data according to the airport's busyness to improve reliability. To improve the application effect, a historical database is established, and the purpose of dynamic prediction is achieved by matching historical data. The experimental results show that the accuracy of dynamic prediction is gradually improved. Within the error range of ±3min, the average maximum prediction accuracy can be up to 86.70%.

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Section: Data Dimensionality Reductionmentioning

confidence: 99%