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Study on redundant data dimension reduction algorithm for cloud computing in the internet of things environment

Qiaoyun Chen & Hui Yao

International Journal of Web Based Communities2025https://doi.org/10.1504/ijwbc.2025.145143article
ABDC C
Weight
0.37

What the paper says

To effectively reduce the dimension of cloud computing redundant data and shorten the time of dimensionality reduction, an algorithm for dimensionality reduction of cloud computing redundant data in the internet of things environment is proposed. Firstly, analyse the architecture of the internet of things environment, and cluster and collect high-dimensional redundant data of cloud computing in the internet of things environment. Secondly, the K-L transform is used to compress the redundant data of cloud computing. Finally, the supervised discriminant projection dimensionality reduction algorithm is used to construct the objective function model of redundant data dimensionality reduction to complete the dimensionality reduction of redundant data. The experimental results show that compared with traditional algorithms, the dimensionality reduction effect of our algorithm is higher, the dimension of redundant data is significantly reduced, and the dimensionality reduction time of our algorithm is significantly reduced when the data size is the same.

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https://doi.org/https://doi.org/10.1504/ijwbc.2025.145143

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@article{qiaoyun2025,
  title        = {{Study on redundant data dimension reduction algorithm for cloud computing in the internet of things environment}},
  author       = {Qiaoyun Chen & Hui Yao},
  journal      = {International Journal of Web Based Communities},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijwbc.2025.145143},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.