TY - GEN
T1 - Field support vector regression
AU - Jiang, Haochuan
AU - Huang, Kaizhu
AU - Zhang, Rui
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - In regression tasks for static data, existing methods often assume that they were generated from an identical and independent distribution (i.i.d.). However, violation can be found when input samples may form groups, each affected by a certain different domain. In this case, style consistency exists within a same group, leading to degraded performance when conventional machine learning models were applied due to the violation of the i.i.d. assumption. In this paper, we propose one novel regression model named Field Support Vector Regression (F-SVR) without i.i.d. assumption. Specifically, we perform a style normalization transformation learning and the regression model learning simultaneously. An alternative optimization with final convergence guaranteed is designed, as well as a transductive learning algorithm, enabling extension on unseen styles during the testing phase. Experiments are conducted on two synthetic as well as two real benchmark data sets. Results show that the proposed F-SVR significantly outperforms many other state-of-the-art regression models in all the used data sets.
AB - In regression tasks for static data, existing methods often assume that they were generated from an identical and independent distribution (i.i.d.). However, violation can be found when input samples may form groups, each affected by a certain different domain. In this case, style consistency exists within a same group, leading to degraded performance when conventional machine learning models were applied due to the violation of the i.i.d. assumption. In this paper, we propose one novel regression model named Field Support Vector Regression (F-SVR) without i.i.d. assumption. Specifically, we perform a style normalization transformation learning and the regression model learning simultaneously. An alternative optimization with final convergence guaranteed is designed, as well as a transductive learning algorithm, enabling extension on unseen styles during the testing phase. Experiments are conducted on two synthetic as well as two real benchmark data sets. Results show that the proposed F-SVR significantly outperforms many other state-of-the-art regression models in all the used data sets.
KW - Field regression
KW - Multi-task learning
KW - Style normalization transformation
KW - Support vector regression
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85035139311&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70087-8_72
DO - 10.1007/978-3-319-70087-8_72
M3 - Conference Proceeding
AN - SCOPUS:85035139311
SN - 9783319700861
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 699
EP - 708
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Li, Yuanqing
A2 - Liu, Derong
A2 - Xie, Shengli
A2 - El-Alfy, El-Sayed M.
A2 - Zhao, Dongbin
PB - Springer Verlag
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
Y2 - 14 November 2017 through 18 November 2017
ER -