Semi-supervised Dimensionality Reduction with Adaptive Neighbors for Intellgent Agriculture

Li Ma, Zengwei Zheng, Feiping Nie, Shenfei Pei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the intelligent agriculture, the popularity of high-dimensional data highlights the importance of dimensionality reduction algorithms. However, Traditional methods struggle with graph quality. The new semi supervised adaptive neighbor dimension reduction (SAN) algorithm addresses this limitation by seamlessly integrating dimension reduction and graph learning within the label propagation framework. SAN simultaneously learns the projection matrix and refines the graph structure to promote mutual enhancement between the two. Extensive experiments conducted on five different datasets have shown that SAN surpasses current state-of-the-art methods and provides a powerful solution for processing complex high-dimensional data in smart agriculture applications.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1657-1662
Number of pages6
ISBN (Electronic)9798331520861
DOIs
StatePublished - 2024
Event10th IEEE Smart World Congress, SWC 2024 - Nadi, Fiji
Duration: 2 Dec 20247 Dec 2024

Publication series

NameProceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications

Conference

Conference10th IEEE Smart World Congress, SWC 2024
Country/TerritoryFiji
CityNadi
Period2/12/247/12/24

Keywords

  • adaptive graph
  • dimensionality reduction
  • intelligent agriculture
  • semi-supervised

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