Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering

Siamak Mehrkanoon, Carlos Alzate, RaghvenPhDa Mall, Rocco Langone, Johan A.K. Suykens

Research output: Contribution to journalArticle

30 Citations (Scopus)


This paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering (KSC) as a core model. A regularized KSC is formulated to estimate the class memberships of data points in a semisupervised setting using the one-versus-all strategy while both labeled and unlabeled data points are present in the learning process. The propagation of the labels to a large amount of unlabeled data points is achieved by adding the regularization terms to the cost function of the KSC formulation. In other words, imposing the regularization term enforces certain desired memberships. The model is then obtained by solving a linear system in the dual. Furthermore, the optimal embedding dimension is designed for semisupervised clustering. This plays a key role when one deals with a large number of clusters.

Original languageEnglish
Article number6822553
Pages (from-to)720-733
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Publication statusPublished - 1 Apr 2015
Externally publishedYes



  • Kernel spectral clustering (KSC)
  • low embedding dimension for clustering
  • multiclass problem
  • semisupervised learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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