TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Regularized extreme learning machine for multi-view semi-supervised action recognition

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut250-262
Sivumäärä13
JulkaisuNeurocomputing
Vuosikerta145
DOI - pysyväislinkit
TilaJulkaistu - 5 joulukuuta 2014
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

In this paper, three novel classification algorithms aiming at (semi-)supervised action classification are proposed. Inspired by the effectiveness of discriminant subspace learning techniques and the fast and efficient Extreme Learning Machine (ELM) algorithm for Single-hidden Layer Feedforward Neural networks training, the ELM algorithm is extended by incorporating discrimination criteria in its optimization process, in order to enhance its classification performance. The proposed Discriminant ELM algorithm is extended, by incorporating proper regularization in its optimization process, in order to exploit information appearing in both labeled and unlabeled action instances. An iterative optimization scheme is proposed in order to address multi-view action classification. The proposed classification algorithms are evaluated on three publicly available action recognition databases providing state-of-the-art performance in all the cases.