基于图的无监督降维算法研究进展

Gang Zhao, Feifei Liu, Jikui Wang, Feiping Nie

科研成果: 期刊稿件文章同行评审

摘要

Graph-based unsupervised dimensionality reduction has become a research hotspot in machine learning. We review typical algorithms in this area and their research progress. We begin by defining the graphs and explaining their construction methods. Next, 12 classical and cutting-edge methods are introduced, categorized into four groups: graph-fixed unsupervised dimensionality reduction algorithms, graph-fixed fast unsupervised dimensionality reduction algorithms, graph-optimized unsupervised dimensionality reduction algorithms, and fast unsupervised dimensionality reduction algorithms based on graph optimization. We then analyze and summarize these methods. Finally, future research directions for graph-based unsupervised dimensionality reduction techniques are discussed.

投稿的翻译标题Research Progress in Graph-based Unsupervised Dimensionality Reduction Algorithms
源语言繁体中文
页(从-至)28-49
页数22
期刊Information and Control
54
1
DOI
出版状态已出版 - 2025

关键词

  • dimensionality reduction
  • graph optimization
  • time complexity
  • unsupervised learning

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