TY - GEN
T1 - Stacked denoising autoencoder for feature representation learning in pose-based action recognition
AU - Budiman, Arif
AU - Fanany, Mohamad Ivan
AU - Basaruddin, T.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/3
Y1 - 2014/2/3
N2 - In this paper, we studied Stacked Denoising Autoencoder(SDA) model for Human pose-based action recognition. We used public dataset Chalearn 2013 which contains Italian body language actions from 27 persons. We studied two model of SDA for pose clustering: 1) Traditional SDA with epoch and Neural Network supervised classifier and 2) Marginalized SDA which faster and ELM supervised classifier. We used supervised classifier by using initial cluster data from K-means. We deployed global tuning that updating the weight during iterative learning.
AB - In this paper, we studied Stacked Denoising Autoencoder(SDA) model for Human pose-based action recognition. We used public dataset Chalearn 2013 which contains Italian body language actions from 27 persons. We studied two model of SDA for pose clustering: 1) Traditional SDA with epoch and Neural Network supervised classifier and 2) Marginalized SDA which faster and ELM supervised classifier. We used supervised classifier by using initial cluster data from K-means. We deployed global tuning that updating the weight during iterative learning.
UR - http://www.scopus.com/inward/record.url?scp=84946686986&partnerID=8YFLogxK
U2 - 10.1109/GCCE.2014.7031302
DO - 10.1109/GCCE.2014.7031302
M3 - Conference contribution
AN - SCOPUS:84946686986
T3 - 2014 IEEE 3rd Global Conference on Consumer Electronics, GCCE 2014
SP - 684
EP - 688
BT - 2014 IEEE 3rd Global Conference on Consumer Electronics, GCCE 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 3rd Global Conference on Consumer Electronics, GCCE 2014
Y2 - 7 October 2014 through 10 October 2014
ER -