Human-Centered Video Feature Selection via mRMR-SCMMCCA for Preference Extraction

被引：3
|
作者：
Ogawa, Takahiro [1 ]
Yamaguchi, Yoshiaki [1 ]
Asamizu, Satoshi [2 ]
Haseyama, Miki [1 ]
机构：
[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Sapporo, Hokkaido 0600814, Japan
[2] Kushiro Coll, Natl Inst Technol, Kushiro, Hokkaido 0840916, Japan
来源：
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2017年 / E100D卷 / 02期
关键词：
Canonical Correlation Analysis; feature selection; preference extraction; viewing behavior;
D O I：
10.1587/transinf.2016EDL8126
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.
引用
收藏
页码：409 / 412
页数：4
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