Neurocomputing, Dec 5, 2014, Vol.145, p.250(13)
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.neucom.2014.05.036 Byline: Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas Abstract: 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. Author Affiliation: Department of Informatics, Aristotle University of Thessaloniki, Box 451, 54124 Thessaloniki, Greece Article History: Received 14 May 2013; Revised 19 December 2013; Accepted 12 May 2014 Article Note: (miscellaneous) Communicated by L. Shao
Artificial Neural Networks ; Algorithms
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