NDLI: Multi-instance Multi-graph Dual Embedding Learning

Content Provider	IEEE Xplore Digital Library
Author	Jia Wu Xingquan Zhu Chengqi Zhang Zhihua Cai
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. & Electr. Eng. & Comput. Sci., Florida Atlantic Univ., Boca Raton, FL, USA (Xingquan Zhu) \|\| Centre for Quantum Comput. & Intell. Syst., Univ. of Technol., Sydney, NSW, Australia (Jia Wu; Chengqi Zhang) \|\| Dept. of Comput. Sci., China Univ. of Geosci., Wuhan, China (Zhihua Cai)
Abstract	Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented by a set of well defined feature values. In this paper, we advance the problem to a multi-instance multi-graph setting, where a bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. To achieve the goal, the main challenge is to properly represent graphs inside each bag and further take advantage of complementary information between instance and graph pairs for learning. In the paper, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative sub graphs discovery process, and (2) embed discovered sub graphs into the instance feature selection process. The dual embedding process results in an optimal representation for each bag to provide combined instance and graph information for learning. Experiments and comparisons on real-world multi-instance multi-graph learning tasks demonstrate the algorithm performance.
Sponsorship	Toshiba
Starting Page	827
Ending Page	836
File Size	1113794
Page Count	10
File Format	PDF
ISBN	9780769551081
ISSN	15504786
DOI	10.1109/ICDM.2013.121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bismuth Vectors Linear programming Educational institutions Kernel Laplace equations Computer science Embedding Classification Multi-instance Multi-graph Graph
Content Type	Text
Resource Type	Article

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