TY - JOUR
T1 - HKBU at MediaEval 2017 Emotional impact of movies task
AU - LIU, Yang
AU - Gu, Zhonglei
AU - Ko, Tobey H.
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.
PY - 2017
Y1 - 2017
N2 - In this paper, we describe our model designed for automatic prediction of emotional impact of movies. Specifically, a two-stage learning framework is proposed. First, the dimensionality reduction techniques are employed to discover the key emotion information embedded in the original feature space. Specifically, we use a classical method principal component analysis (PCA) and a new algorithm biased discriminant embedding (BDE) to learn the subspace. After dimensionality reduction, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.
AB - In this paper, we describe our model designed for automatic prediction of emotional impact of movies. Specifically, a two-stage learning framework is proposed. First, the dimensionality reduction techniques are employed to discover the key emotion information embedded in the original feature space. Specifically, we use a classical method principal component analysis (PCA) and a new algorithm biased discriminant embedding (BDE) to learn the subspace. After dimensionality reduction, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.
UR - http://www.scopus.com/inward/record.url?scp=85035064792&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85035064792
SN - 1613-0073
VL - 1984
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2017 Multimedia Benchmark Workshop, MediaEval 2017
Y2 - 13 September 2017 through 15 September 2017
ER -