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Minimum Variance Extreme Learning Machine for human action recognition

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
KustantajaThe Institute of Electrical and Electronics Engineers, Inc.
Sivut5427-5431
Sivumäärä5
ISBN (painettu)9781479928927
DOI - pysyväislinkit
TilaJulkaistu - 2014
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italia
Kesto: 4 toukokuuta 20149 toukokuuta 2014

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
MaaItalia
KaupunkiFlorence
Ajanjakso4/05/149/05/14

Tiivistelmä

In this paper we propose an algorithm for Single-hidden 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.