Grid-DPC: Improved density peaks clustering based on spatial grid walk

Liang, Bo; Cai, Jianghui; Yang, Haifeng

doi:10.1007/s10489-022-03705-y

Cited by 5 publications

(2 citation statements)

References 50 publications

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“…In this case, each client can transform different model structures into the same structure through some mappings approach for federation. While in the local model training, each client can selectively learn the federated model to help guide the training of the local model [110], so as to build the local personalized model.…”

Section: Data Heterogeneitymentioning

confidence: 99%

A survey on federated learning: challenges and applications

Zhang

Lan

et al. 2022

Int. J. Mach. Learn. & Cyber.

Self Cite

102

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y impact on human life [1][2][3]. As great progress has been made in AI technology in recent years, various application fields have stepped into intelligence [4,5]. On the road of AI development, models, computing power, chip performance, and other technical issues have been the focus of academic research, so that AI technology can continue to evolve. For machines to truly approach the level of human thought, they need to be trained with vast amounts of real data [6,7]. However, cloud computing power, data security, data silos and other risks will inevitably become constraints for AI to win user trust, collect private data, and achieve largescale implementation [8]. Therefore, it is an urgent need for a practical and effective technique to alleviate the above problems and make the AI full of vitality again. Under this background, the concept of "federated learning (FL) " came into being.The notion of FL is first proposed by Google in 2016, mainly to make android mobile phone users update models locally without revealing private personal data [9]. After then, Google implemented an application-oriented FL system. The designed FL system, which focused on running federated average (FedAvg) algorithms on mobile phones, can perform federated analytics and be applied to monitor statistics for large-scale cluster equipment without recording raw device data to the cloud server. FL is one of the most Zhihua Cui

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Section: Data Heterogeneitymentioning

confidence: 99%

A survey on federated learning: challenges and applications

Zhang

Lan

et al. 2022

Int. J. Mach. Learn. & Cyber.

Self Cite

102

View full text Add to dashboard Cite

show abstract

“…The reconstruction problem of CS is an underdetermined problem, which theoretically has infinite solutions, although traditional CS algorithms have carried out a lot of research on reconstruction algorithms, including convex optimization algorithms, [44][45][46] non-convex optimization algorithms, [47][48][49] and nature-inspired algorithm. [50][51][52] In addition, the design of reconstruction algorithms can also draw on techniques such as target detection, [53][54][55] target localization, [56][57][58] image enhancement [59][60][61] and feature fusion 62 to improve the reconstruction accuracy.…”

Section: Reconstruction Algorithmmentioning

confidence: 99%

Survey on compressed sensing reconstruction method for 3D data

Zhang

Xie

2022

Concurrency and Computation

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The information society has higher and higher requirements for the collection, transmission and storage of digital signals, and signal utilization efficiency has become an increasingly important part of the digital signal processing process. The traditional digital signal processing needs to satisfy the Nyquist sampling theorem to ensure the restoration of the signal, while the digital signal processing method based on compressed sensing can sample and reconstruct the signal under the conditions that much lower than the Nyquist sampling theorem. Undoubtedly, the utilization efficiency of digital signals has been greatly accelerated. In this article, taking hyperspectral images and videos as examples, we review the basic theory, reconstruction model, and reconstruction algorithm of 3D data compressed sensing. We analyze, summarize, and discuss the existing literature. Finally, the research status of compressive sensing reconstruction methods for hyperspectral images and videos is compared, and several important research directions for compressive sensing reconstruction of 3D data in the future are proposed.

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Exploring the evolutionary patterns of urban tourist mobility within a day: A three‐step analysis framework

Huang,

Li,

Zheng

et al. 2024

Journal of Tourism Research

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Despite the importance of tourist mobility for urban tourism, mechanisms as to how these tourist activities shift throughout the day remain scarcely explored. This study fills this gap by providing a three‐step analysis framework. First, aggregated trajectory data were transformed into a directed weighted network. Then, a new community detection method was proposed to capture the spatial interactions, and two approaches were employed to quantify the evolution patterns of tourist mobility. Finally, the capability of the framework and its potential applications were explored and implemented through a case study in Xiamen, China.

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Grid-DPC: Improved density peaks clustering based on spatial grid walk

Cited by 5 publications

References 50 publications

A survey on federated learning: challenges and applications

A survey on federated learning: challenges and applications

Survey on compressed sensing reconstruction method for 3D data

Exploring the evolutionary patterns of urban tourist mobility within a day: A three‐step analysis framework

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