Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors

Zhengguo Yang, Jikui Wang, Qiang Li, Jihai Yi, Xuewen Liu, Feiping Nie

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Graph-based dimensionality reduction methods have attracted much attention for they can be applied successfully in many practical problems such as digital images and information retrieval. Two main challenges of these methods are how to choose proper neighbors for graph construction and make use of global and local information when conducting dimensionality reduction. In this paper, we want to tackle these two challenges by presenting an improved graph optimization approach for unsupervised dimensionality reduction. Our method can deal with dimensionality reduction and graph construction at the same time, which doesn’t need to construct an affinity graph beforehand. On the other hand, by integrating the advantages of the orthogonal local preserving projections and principal component analysis, both the local and global information of the original data are considered in dimensionality reduction in our approach. Eventually, we learn the sparse affinity graph by considering probabilistic neighbors, which is optimal and suitable for classification. To testify the superiority of our approach, we carry out some experiments on several publicly available UCI and image data sets, and the results have demonstrated the effectiveness of our approach.

Original languageEnglish
Pages (from-to)2348-2361
Number of pages14
JournalApplied Intelligence
Volume53
Issue number2
DOIs
StatePublished - Jan 2023

Keywords

  • Locality preserving projections
  • Principal component analysis
  • Probabilistic neighbors
  • Unsupervised dimensionality reduction

Fingerprint

Dive into the research topics of 'Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors'. Together they form a unique fingerprint.

Cite this