Label propagation using sparse approximated nearest feature space embedding

Jian Wen Tao, Fu Lai Korris Chung, Shi Tong Wang, Qi Fu Yao

Research output: Journal article publicationJournal articleAcademic researchpeer-review


There exist several problems in existing graph-based semi-supervised learning (GSSL) methods such as model parameters sensitiveness and insufficient discriminative information in data space, etc. To address those issues, this paper proposes a sparse approximated nearest feature space embedding label propagation (SANFSP) algorithm, which is inspired by both ideas of nearest feature space embedding and that of sparse representation. SANFSP first sparsely reconstructs data from original space using its feature space embedding projection images, and then measures the similarity between original data and its sparse approximated nearest feature space embedding projection points, thus proposing a sparse approximated nearest feature space embedding regularizer. At last, SANFSP complets label propagation procedure by using classical label propagation algorithm. The study also derives an easy way to extend SANFSP to out-of-sample data. Promising experimental results are obtained on several toy and real-world classification tasks such as face recognition, visual object recognition and digit classification.
Original languageEnglish
Pages (from-to)1239-1254
Number of pages16
JournalRuan Jian Xue Bao/Journal of Software
Issue number6
Publication statusPublished - 1 Jan 2014


  • Label propagation
  • Nearest feature space
  • Semi-supervised learning
  • Sparse representation

ASJC Scopus subject areas

  • Software

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