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可迁移测度准则下的协变量偏移修正多源集成方法

杨兴明 吴克伟 孙永宣 谢昭

杨兴明, 吴克伟, 孙永宣, 谢昭. 可迁移测度准则下的协变量偏移修正多源集成方法[J]. 电子与信息学报, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323
引用本文: 杨兴明, 吴克伟, 孙永宣, 谢昭. 可迁移测度准则下的协变量偏移修正多源集成方法[J]. 电子与信息学报, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323
Yang Xing-ming, Wu Ke-wei, Sun Yong-xuan, Xie Zhao. Modified Covariate-shift Multi-source Ensemble Method in Transferability Metric[J]. Journal of Electronics and Information Technology, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323
Citation: Yang Xing-ming, Wu Ke-wei, Sun Yong-xuan, Xie Zhao. Modified Covariate-shift Multi-source Ensemble Method in Transferability Metric[J]. Journal of Electronics and Information Technology, 2015, 37(12): 2913-2920. doi: 10.11999/JEIT150323

可迁移测度准则下的协变量偏移修正多源集成方法

doi: 10.11999/JEIT150323
基金项目: 

国家自然科学基金(60905005, 61273237)

Modified Covariate-shift Multi-source Ensemble Method in Transferability Metric

Funds: 

The National Natural Science Foundation of China (60905005, 61273237)

  • 摘要: 迁移学习通过充分利用源域共享知识,实现对目标域的小样本问题求解,然而,对训练和测试样本分布差异测度仍然是该领域的主要挑战。该文针对多源迁移学习算法中,由于源域选择和源域辅助样本选择不当引起的负迁移问题进行研究,提出一种可迁移测度准则下的协变量偏移修正多源集成方法。首先,根据源域和目标域之间的协变量偏移原则,利用联合概率的密度估计,定义辅助样本的可迁移测度,验证目标域和源域在数据空间中标记分布的一致性。其次,在多源域选择阶段,引入非迁移判别过程,提高了源域知识的迁移准确性。最后,在Caltech 256数据集中,验证了Gist特征知识表示和迁移的有效性,分析了多种条件下的辅助样本选择和源域选择的有效性。实验结果表明所提算法可有效降低负迁移现象的发生,获得更好的迁移学习性能
  • [1] Tommasi T, OrabonaF, and Caputo B. Learning categories from few examples with multi model knowledge transfer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(5): 928941.
    [2] Yao Y and Doretto G. Boosting for transfer learning with multiple sources[C]. Proceedings of Computer Vision and Pattern Recognition, San Francisco, 2010: 18551862.
    [3] Long M S, Wang J M, Ding G G, et al.. Adaptation regularization a general framework for transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(5): 10761089.
    [4] Lin D, An X, and Zhang J. Double-bootstrapping source data selection for instance-based transfer learning[J]. Pattern Recognition Letters, 2013, 34(11): 12791285.
    [5] Kuzborskij I, Orabona F, and Caputo B. From N to N+1: multiclass transfer incremental learning[C]. Proceedings of Computer Vision and Pattern Recognition, Portland, 2013: 33583365.
    [6] Zhu Y, Chen Y Q, Lu Z Q, et al.. Heterogeneous transfer learning for image classification[C]. Proceedings of AAAI Conference on Artificial Intelligence, San Francisco, 2011: 13041309.
    [7] 张倩, 李明, 王雪松, 等. 一种面向多源领域的实例迁移学习[J]. 自动化学报, 2014, 40(6): 1176-1183.
    [8] Zhang Qian, Li Ming, Wang Xue-song, et al.. Instance-based transfer learning for multi-source domains[J]. Acta Automatica Sinica, 2014, 40(6): 11761183.
    [9] Pang J, Huang Q, Yan S, et al.. Transferring boosted detectors towards viewpoint and scene adaptiveness[J]. IEEE Transactions on Image Processing, 2011, 20(5): 13881400.
    [10] Li G, Qin L, Huang Q, et al.. Treat samples differently: object tracking with semi-supervised online CovBoost[C]. Proceedings of International Conference on Computer Vision, Barcelona, 2011: 627634.
    [11] Qi G J, Aggarwal C, Rui Y, et al.. Towards cross-category knowledge propagation for learning visual concepts[C]. Proceedings of Computer Vision and Pattern Recognition, Colorado Springs, 2011: 897904.
    [12] Chu W S, Torre F D, and CohnJ F. Selective transfer machine for personalized facial action unit detection[C]. Proceedings of Computer Vision and Pattern Recognition, Portland, 2013: 35153522.
    [13] Yang S Z, Hou C P, Zhang C S, et al.. Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning[J]. Neural Computing and Applications, 2013, 23(2): 541559.
    [14] 方耀宁, 郭云飞, 丁雪涛, 等. 一种基于标签迁移学习的改进正则化奇异值分解推荐算法[J]. 电子与信息学报, 2013, 35(12): 30463050.
    [15] Fang Yao-ning, Guo Yun-fei, Ding Xue-tao, et al.. An improved regularized singular value decomposition recommender algorithm based on tag transfer learning[J]. Journal of Electronics Information Technology, 2013, 35(12): 30463050.
    [16] Gopalan R. Learning cross-domain information transfer for location recognition and clustering[C]. Proceedings of Computer Vision and Pattern Recognition, Portland, 2013: 731738.
    [17] Luo Y, Liu T L, Tao D C, et al.. Decomposition-based transfer distance metric learning for image classification[J]. IEEE Transactions on Image Processing, 2014, 23(9): 37893801.
    [18] Long M S, Wang J M, Ding G G, et al.. Transfer learning with graph co-regularization[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 18051818.
    [19] 洪佳明, 印鉴, 黄云, 等. TrSVM: 一种基于领域相似性的迁移学习算法[J]. 计算机研究与发展, 2011, 48(10): 18231830.
    [20] Hong Jia-ming, Yin Jian, Huang Yun, et al.. TrSVM: a transfer learning algorithm using domain similarity[J]. Journal of Computer Research and Development, 2011, 48(10): 18231830.
    [21] Seah C W, Tsang I W, and Ong Y S. Transfer ordinal label learning[J]. IEEE Transactions on Neural Networkand Learning System, 2013, 24(11): 18631876.
    [22] 许敏, 王士同, 史荧中. 一种新的面向迁移学习的L2核分类器[J]. 电子与信息学报, 2013, 35(9): 2059-2065.
    [23] Xu Min, Wang Shi-tong, and Shi Ying-zhong. A novel transfer-learning-oriented L2 kernel classifier[J]. Journal of Electronics Information Technology, 2013, 35(9): 20592065.
    [24] Long M S, Wang J M, Ding G G, et al.. Transfer feature learning with joint distribution adaptation[C]. Proceedings of International Conference on Computer Vision, Sydney, 2013: 22002207.
    [25] Patricia N and Caputo B. Learning to learn, from transfer learning to domain adaptation: a unifying perspective[C]. Proceedings of Computer Vision and Pattern Recognition, Columbus, 2014: 14421449.
    [26] Zhang B, Wang Y, Wang Y, et al.. Stable learning in coding space for multi-class decoding and its extension for multi-class hypothesis transfer learning[C]. Proceedings of Computer Vision and Pattern Recognition, Columbus, 2014: 10751081.
    [27] Gretton A, Smola A, Huang J, et al.. Covariate Shift by Kernel Mean Matching[M]. Cambridge: MIT Press, 2009: 131160.
    [28] Huang P P, Wang G, and Qin S Y. Boosting for transfer learning from multiple data sources[J]. Pattern Recognition Letters, 2012, 33(5): 568579.
    [29] Nie Q F, Jin L Z, and Fei S M. Probability estimation for multi-class classification using AdaBoost[J]. Pattern Recognition, 2014, 47(12): 39313940.
    [30] Sugiyama M, Krauledat M, and Mller K R. Covariate shift adaptation by importance weighted cross validation[J]. The Journal of Machine Learning Research, 2007, 8(1): 9851005.
    [31] Choi M J, Lim J J, Torralba A, et al.. Exploiting hierarchical context on a large database of object categories[C]. IEEE Conference on Computer Vision Pattern Recognition, San Frencisco, CA, 2010: 129136.
    [32] Han Y and Liu G. Biologically inspired task oriented gist model for scene classification[J]. Computer Vision and Image Understanding, 2013, 117(1): 7695.
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    出版历程
    • 收稿日期:  2015-03-17
    • 修回日期:  2015-08-13
    • 刊出日期:  2015-12-19

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