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Index migration directed by lattice reduction for feature data fusion

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Abstract

From the opinion of data representation, feature data fusion is a process of transforming the redundant source representation into the concise object representation by removing redundant data from source feature data. Based on the structured lattice representation of source feature data, this paper addresses the transformation of data representation by reducing the quantum representations of lattice nodes, and then proposes the fusion method based on lattice reduction directed index migration. This method classifies all lattice nodes into different node subsets through the gradual migration of the indexes of the qubits in different lattice nodes. The source lattice nodes in a subset will be fused into a new object node based on their measurement probabilities. The experimental data evaluation results demonstrate that the proposed fusion method can obtain concise and reliable fusion results for intelligent decision-making.

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Acknowledgements

This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY21F020014), the Zhejiang Province Public Welfare Technology Application Research Project (No. LGF20F020006), the Zhejiang Provincial Key Research and Development Program (No. 2021C01114), and the National Key R&D Program of China (No. 2021YFC3320301).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Weimin Peng, Jing Chen & Haitao Xu

  2. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China

    Aihong Chen

Authors
  1. Weimin Peng
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  2. Aihong Chen
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  3. Jing Chen
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  4. Haitao Xu
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Correspondence to Weimin Peng.

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Appendix 1. Fusion results for CIMLR and QIMLR on Burlington and Des Moines datasets

Appendix 1. Fusion results for CIMLR and QIMLR on Burlington and Des Moines datasets

Tables 4, 5, 6 and 7

T1-T8: average maximum, average minimum, historical maximum, historical minimum, weekly average, weekly deviation, days over 90, and days under 32 temperatures; P1-P7: weekly accumulation, weekly deviation, 24 h maximum, monthly accumulation, annual accumulation, days over 0.01 inches, and days over 0.5 inches precipitations; RH1-RH2: average maximum and minimum relative humidity.

Table 4 Fusion Results for CIMLR on Burlington dataset
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Table 5 Fusion Results for QIMLR on Burlington dataset
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Table 6 Fusion Results for CIMLR on Des Moines dataset
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Table 7 Fusion Results for QIMLR on Des Moines dataset
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Peng, W., Chen, A., Chen, J. et al. Index migration directed by lattice reduction for feature data fusion. Appl Intell 53, 3291–3303 (2023). https://doi.org/10.1007/s10489-022-03588-z

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