A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang, Peng; She, Kun

doi:10.1155/2020/6748056

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…In recent years, several researchers further explored the potential of the gravitational clustering method, by regarding each data point as an object with mass and associating a data gravitational force with each data point generated by its neighbors [21]. Based on the data gravitational force, the position of each data point would be updated at each iteration and aggregated into clusters [22]. The use of data gravitational force (including its magnitude and direction) in clustering can be regarded as a variant of density-based clustering method [22].…”

Section: A Gravity-based Photon Density Modelmentioning

confidence: 99%

“…Based on the data gravitational force, the position of each data point would be updated at each iteration and aggregated into clusters [22]. The use of data gravitational force (including its magnitude and direction) in clustering can be regarded as a variant of density-based clustering method [22]. To some extent, the data gravitational force can be considered as a similarity measure, which takes both distance and direction among data points into account [21].…”

Section: A Gravity-based Photon Density Modelmentioning

confidence: 99%

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Detection of Signal and Ground Photons From ICESat-2 ATL03 Data

Tian

Shan

2023

IEEE Trans. Geosci. Remote Sensing

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The Advanced Topographic Laser Altimeter System (ATLAS) laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2) can measure the elevation of the Earth's surface with unprecedented spatial detail. However, the quality of the derived signal and ground photons depends on the signal-to-noise ratio and canopy coverage. Current algorithms underperform for data collected during daytime over mountain areas with dense canopy. We demonstrate a novel procedure for signal photon detection and subsequent ground photon detection from ICESat-2 ATL03 data. We first introduce a gravity-based density model to characterize the anisotropic properties of photon distribution. Through jointly using the photon densities from the weak-strong beam pair, we are able to find key photons that have high probability being signals. A directional regional growing approach then takes these key photons as seeds to label all remaining signal photons. Finally, we introduce a weighted iterative median filter (WIMF) algorithm to identify ground photons whose height is closest to the estimated ground surface. A total of 36 ATL03 beams of two entire counties in USA are used for test and evaluation. Compared to the ATL03 and ATL08 algorithms, our signal photon finding method is more robust to the variation of topography, canopy coverage, and data collection time. Remarkably, the mislabeling caused by the after-pulsing effect does not present in our detected signal photons. Comparing current ATL03 and ATL08 products, the detected ground photons from our method are more consistent with reference to the 3DEP DEM, especially for strong beam data collected during daytime in dense canopy, high relief areas.

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Section: A Gravity-based Photon Density Modelmentioning

confidence: 99%

Section: A Gravity-based Photon Density Modelmentioning

confidence: 99%

Detection of Signal and Ground Photons From ICESat-2 ATL03 Data

Tian

Shan

2023

IEEE Trans. Geosci. Remote Sensing

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“…The universal gravitation algorithm has the advantages of strong global optimization ability and simple process [16,17]. It can be well used in practical problems and can highlight its superiority in the treatment of nonlinear problems.…”

Section: Improved Uga For Optimizing Kelmmentioning

confidence: 99%

A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

Yin¹,

Li²,

Laghari³

et al. 2021

ICST Transactions on Scalable Information Systems

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Network intrusion can enter the network through informal channels. Some illegal users utilize Trojans and selfprogrammed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information. Network intrusion seriously harms the security of network information and the legitimate rights of users. Therefore, a bagging strategy-based kernel extreme learning machine for complex network intrusion detection is presented in this paper. This method adopts a bagging strategy to train several sub-kernel extreme learning machines independently. Then the integrated gain of above machines is measured based on the margin distance minimization (MDM) criterion. Selected machines with high gain degree are selected for selective integration to obtain selective integrated learners with strong generalization ability and high efficiency. Then an improved universal gravitation search algorithm is used to optimize the kernel parameters. Meanwhile, a sub-kernel extreme learning machine online update strategy based on incremental learning of batch samples is introduced to realize the online update of intrusion detection model, so that the proposed detection method can effectively be adapted to the changes of complex network environment. Finally, experiments illustrate that the proposed method has better effect on network intrusion detection in terms of detection accuracy and speed, especially for unknown network intrusion connection events, the response speed is fast, the false alarm rate is low.

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“…The study of data driven projection algorithms suggest that a more fundamental approach which balances the forces exerted by samples of multiple classes could provide generalizable results. Inspired by different laws of Physics, researchers have proposed different techniques for data projection, classification, and clustering [3], [9], [12], [13], [18], [24], [26], [33]. Inspired by Newton's universal law of gravitation, Shi et al In input space, the classes are overlapped while in equilibrium space, the separation among the classes is optimal [23] presented a data preprocessing technique to optimize the inner structure of the data to create condensed and widely separated clusters.…”

Section: Introductionmentioning

confidence: 99%

Class Equilibrium using Coulomb's Law

Chhabra¹,

Majumdar²,

Vatsa³

et al. 2021

Preprint

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Projection algorithms learn a transformation function to project the data from input space to the feature space, with the objective of increasing the inter-class distance. However, increasing the inter-class distance can affect the intra-class distance. Maintaining an optimal inter-class separation among the classes without affecting the intra-class distance of the data distribution is a challenging task. In this paper, inspired by the Coulomb's law of Electrostatics, we propose a new algorithm to compute the equilibrium space of any data distribution where the separation among the classes is optimal. The algorithm further learns the transformation between the input space and equilibrium space to perform classification in the equilibrium space. The performance of the proposed algorithm is evaluated on four publicly available datasets at three different resolutions. It is observed that the proposed algorithm performs well for low-resolution images.

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A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Cited by 10 publications

References 38 publications

Detection of Signal and Ground Photons From ICESat-2 ATL03 Data

Detection of Signal and Ground Photons From ICESat-2 ATL03 Data

A Bagging Strategy-Based Kernel Extreme Learning Machine for Complex Network Intrusion Detection

Class Equilibrium using Coulomb's Law

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