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

OGAWA, Takahiro; YAMAGUCHI, Yoshiaki; ASAMIZU, Satoshi; HASEYAMA, Miki

doi:10.1587/transinf.2016EDL8126


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

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65544

Title:	Human-Centered Video Feature Selection via mRMR-SCMMCCA for Preference Extraction
Authors:	OGAWA, Takahiro Browse this author →KAKEN DB
	YAMAGUCHI, Yoshiaki Browse this author
	ASAMIZU, Satoshi Browse this author
	HASEYAMA, Miki Browse this author →KAKEN DB
Keywords:	Canonical Correlation Analysis
	feature selection
	preference extraction
	viewing behavior
Issue Date:	2017
Publisher:	IEICE
Journal Title:	IEICE Transactions on Information and Systems
Volume:	E100.D
Issue:	2
Start Page:	409
End Page:	412
Publisher DOI:	10.1587/transinf.2016EDL8126
Abstract:	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.
Rights:	Copyright ©2017 The Institute of Electronics, Information and Communication Engineers
Relation:	https://search.ieice.org/
Type:	article
URI:	http://hdl.handle.net/2115/65544
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小川貴弘

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