Graph Embedded Extreme Learning Machine

Ioannis Pitas, Alexandros Iosifidis, Anastasios Tefas

Research output: Contribution to journalArticle (Academic Journal)peer-review

95 Citations (Scopus)
578 Downloads (Pure)


In this paper, we propose a novel extension of the Extreme Learning Machine algorithm for Single-hidden Layer Feedforward Neural network training that is able to incorporate Subspace Learning (SL) criteria on the optimization process
followed for the calculation of the network’s output weights. The proposed Graph Embedded Extreme Learning Machine (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the Graph Embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces. We evaluate the proposed approach on eight standard classification problems and nine publicly available datasets designed for three problems related to human behaviour analysis, i.e., the recognition of human face, facial expression and activity. Experimental results denote the effectiveness of the proposed approach, since it outperforms other ELM-based classification schemes in all the cases.
Original languageEnglish
Pages (from-to)311-324
JournalIEEE Transactions on Cybernetics
Issue number1
Early online date2 Mar 2015
Publication statusPublished - 1 Jan 2016


  • Extreme Learning Machine
  • Graph Embedding
  • Human action recognition
  • Facial Image Classification


Dive into the research topics of 'Graph Embedded Extreme Learning Machine'. Together they form a unique fingerprint.

Cite this