Minimum Variance Extreme Learning Machine for human action recognition

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

23 Citations (Scopus)
199 Downloads (Pure)

Abstract

Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a low-dimensional space where classification is performed by a linear classifier, we extend the Extreme Learning Machine (ELM) algorithm in order to exploit the training data dispersion in its optimization process. The proposed Minimum Variance Extreme Learning Machine classifier is evaluated in human action recognition, where we compare its performance with that of other ELM-based classifiers, as well as the kernel Support Vector Machine classifier.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)
Subtitle of host publicationProceedings of a meeting held 4-9 May 2014, Florence, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5427-5431
Number of pages5
ISBN (Electronic)9781479928927
ISBN (Print)9781479928941
DOIs
Publication statusPublished - Sep 2014
Event2014 IEEE International Conference on Acoustics, Speech and Signal Processing - Florence, Italy
Duration: 4 May 20149 May 2014

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1520-6149

Conference

Conference2014 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP '14
CountryItaly
CityFlorence
Period4/05/149/05/14

Keywords

  • Single-hidden Layer Feedforward Neural networks
  • Extreme Learning Machine
  • Human Action Recognition
  • Classification

Fingerprint

Dive into the research topics of 'Minimum Variance Extreme Learning Machine for human action recognition'. Together they form a unique fingerprint.

Cite this