NDLI: Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

Content Provider	IEEE Xplore Digital Library
Author	Jingbin Wang Yihua Zhou Kanghong Duan Jim Jing-Yan Wang Bensmail, H.
Copyright Year	2015
Description	Author affiliation: North China Sea Marine Tech. Support Center, State Oceanic Adm., Qingdao, China (Kanghong Duan) \|\| Dept. of Mech. Eng. & Mech., Lehigh Univ., Bethlehem, PA, USA (Yihua Zhou) \|\| Qatar Comput. Res. Inst., Doha, Qatar (Bensmail, H.) \|\| Nat. Time Service Center, Xi'an, China (Jingbin Wang) \|\| Comput., Electr. & Math. Sci. & Eng. Div., King Abdullah Univ. of Sci. & Technol., Thuwal, Saudi Arabia (Jim Jing-Yan Wang)
Abstract	In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., An image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.
Starting Page	1882
Ending Page	1888
File Size	244488
Page Count	7
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.329
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fasteners Training Loss measurement Optimization Iterative methods Matrix decomposition Linear programming Supervised learning Multiple modal learning Cross-modal factor analysis
Content Type	Text
Resource Type	Article

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