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Laplacian One Class Extreme Learning Machine for Human Action Recognition

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)
ISBN (elektroninen)9781509037247
DOI - pysyväislinkit
TilaJulkaistu - 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Workshop on Multimedia Signal Processing -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2473-3628

Conference

ConferenceIEEE International Workshop on Multimedia Signal Processing
Ajanjakso1/01/00 → …

Tiivistelmä

A novel OCC method for human action recognition namely the Laplacian One Class Extreme Learning Machines is presented. The proposed method exploits local geometric data information within the OC-ELM optimization process. It is shown that emphasizing on preserving the local geometry of the data leads to a regularized solution, which models the target class more efficiently than the standard OC-ELM algorithm. The proposed method is extended to operate in feature spaces determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions. Its superior performance against other OCC options is consistent among five publicly available human action recognition datasets.