TY - GEN
T1 - Regularized supervised topic model for continuous emotion analysis
AU - Lade, Prasanth
AU - Demakethepalli Venkateswara, Hemanth
AU - Panchanathan, Sethuraman
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1116360. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation
Publisher Copyright:
© 2015 IEEE.
PY - 2016/3/2
Y1 - 2016/3/2
N2 - Dimension reduction techniques form the core of predictive analytics systems and they help us create a new feature space that is more helpful in predicting response variables. But these techniques do not necessarily guarantee a better predictive capability as most of them are unsupervised, especially in regression learning. In regression analysis literature, supervised dimension reduction techniques have not been explored much and in this work we provide a solution to this through probabilistic topic models. In this work, we have shown that the double mixture structure of Latent Dirichlet Allocation topic model helps us 1) to visualize feature patterns, and 2) to project features onto a topic simplex that is more predictive of responses, when compared to popular techniques like PCA and KernelPCA. Until now, topic models have not been explored in a supervised context of video analysis and in this work we introduce a Regularized supervised topic model (RSLDA) that models video and audio features and has outperformed supervised dimension reduction techniques like SPCA and Correlation based feature selection algorithms. All the models discussed in this work have been evaluated to predict continuous human emotions from video data.
AB - Dimension reduction techniques form the core of predictive analytics systems and they help us create a new feature space that is more helpful in predicting response variables. But these techniques do not necessarily guarantee a better predictive capability as most of them are unsupervised, especially in regression learning. In regression analysis literature, supervised dimension reduction techniques have not been explored much and in this work we provide a solution to this through probabilistic topic models. In this work, we have shown that the double mixture structure of Latent Dirichlet Allocation topic model helps us 1) to visualize feature patterns, and 2) to project features onto a topic simplex that is more predictive of responses, when compared to popular techniques like PCA and KernelPCA. Until now, topic models have not been explored in a supervised context of video analysis and in this work we introduce a Regularized supervised topic model (RSLDA) that models video and audio features and has outperformed supervised dimension reduction techniques like SPCA and Correlation based feature selection algorithms. All the models discussed in this work have been evaluated to predict continuous human emotions from video data.
KW - Dimension reduction
KW - Regression analysis
KW - Topic models
UR - http://www.scopus.com/inward/record.url?scp=84969590914&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969590914&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2015.219
DO - 10.1109/ICMLA.2015.219
M3 - Conference contribution
AN - SCOPUS:84969590914
T3 - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
SP - 739
EP - 744
BT - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Y2 - 9 December 2015 through 11 December 2015
ER -