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Lattice structure based metric for feature data fusion

Author

Listed:
  • Weimin Peng
  • Aihong Chen
  • Jing Chen
  • Haitao Xu

Abstract

For stably reducing data redundancy which has negative impact on data processing, this paper integrates the ideas from lattice structure and develops a lattice structure based metric for feature fusion. The metric distance between two lattice nodes, which is a key factor for the lattice structure based metric, is determined by the shortest path length from these two nodes to their nearest descendant node in lattice structure. Then, based on the metrics between lattice nodes, the source feature dataset is divided into different subsets following the principles of minimum metric precedence and gradual expansion. The duplicate feature data in a subset is fused into an object data unit by using the median operation. The experimental results show that the proposed feature fusion method can better improve the completeness and conciseness of the existing feature data stably and has wide range of applications in the related fields on data processing.

Suggested Citation

  • Weimin Peng & Aihong Chen & Jing Chen & Haitao Xu, 2019. "Lattice structure based metric for feature data fusion," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(9), pages 1731-1741, July.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:9:p:1731-1741
    DOI: 10.1080/00207721.2019.1623338
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