Semi-supervised classification based on random subspace dimensionality reduction

Guoxian Yu, Guoji Zhang, Carlotta Domeniconi, Zhiwen Yu, Jia You

Research output: Journal article publicationJournal articleAcademic researchpeer-review

95 Citations (Scopus)


Graph structure is vital to graph based semi-supervised learning. However, the problem of constructing a graph that reflects the underlying data distribution has been seldom investigated in semi-supervised learning, especially for high dimensional data. In this paper, we focus on graph construction for semi-supervised learning and propose a novel method called Semi-Supervised Classification based on Random Subspace Dimensionality Reduction, SSC-RSDR in short. Different from traditional methods that perform graph-based dimensionality reduction and classification in the original space, SSC-RSDR performs these tasks in subspaces. More specifically, SSC-RSDR generates several random subspaces of the original space and applies graph-based semi-supervised dimensionality reduction in these random subspaces. It then constructs graphs in these processed random subspaces and trains semi-supervised classifiers on the graphs. Finally, it combines the resulting base classifiers into an ensemble classifier. Experimental results on face recognition tasks demonstrate that SSC-RSDR not only has superior recognition performance with respect to competitive methods, but also is robust against a wide range of values of input parameters.
Original languageEnglish
Pages (from-to)1119-1135
Number of pages17
JournalPattern Recognition
Issue number3
Publication statusPublished - 1 Mar 2012


  • Dimensionality reduction
  • Ensembles of classifiers
  • Graph construction
  • Random subspaces
  • Semi-supervised classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Semi-supervised classification based on random subspace dimensionality reduction'. Together they form a unique fingerprint.

Cite this