Semi-Supervised and Unsupervised Extreme Learning Machines

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or…

• NN
  National Natural Science Foundation of China

  Award: 61273233
• SR
  Specialized Research Fund for the Doctoral Program of Higher Education of China

  Awards: 20130002130010, 20120002110035