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    Multi-instance dictionary learning via multivariate performance measure optimization

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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Tsang, Ivor Wai-Hung
    Cui, Xuefeng
    Lu, Zhiwu
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-12-29
    Online Publication Date
    2016-12-29
    Print Publication Date
    2017-06
    Permanent link to this record
    http://hdl.handle.net/10754/622623
    
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    Abstract
    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.
    Citation
    Wang JJ-Y, Tsang IW-H, Cui X, Lu Z, Gao X (2016) Multi-instance dictionary learning via multivariate performance measure optimization. Pattern Recognition. Available: http://dx.doi.org/10.1016/j.patcog.2016.12.023.
    Sponsors
    We are grateful to Prof. Zhi-Hua Zhou for the fruitful discussions. The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).
    Publisher
    Elsevier BV
    Journal
    Pattern Recognition
    DOI
    10.1016/j.patcog.2016.12.023
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0031320316304435
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.patcog.2016.12.023
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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