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首页 > 过刊浏览>2013年第24卷第11期 >2642-2655. DOI:10.3724/SP.J.1001.2013.04464
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辅助信息自动生成的时间序列距离度量学习
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国家自然科学基金(61139002)


Distance Metric Learning Based on Side Information Autogeneration for Time Series
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    摘要:

    对于时间序列聚类任务而言,一个有效的距离度量至关重要.为了提高时间序列聚类的性能,考虑借助度量学习方法,从数据中学习一种适用于时序聚类的距离度量.然而,现有的度量学习未注意到时序的特性,且时间序列数据存在成对约束等辅助信息不易获取的问题.提出一种辅助信息自动生成的时间序列距离度量学习(distancemetric learning based on side information autogeneration for time series,简称SIADML)方法.该方法利用动态时间弯曲(dynamic time warping,简称DTW)距离在捕捉时序特性上的优势,自动生成成对约束信息,使习得的度量尽可能地保持时序之间固有的近邻关系.在一系列时间序列标准数据集上的实验结果表明,采用该方法得到的度量能够有效改善时间序列聚类的性能.

    Abstract:

    An effective distance metric is essential for time series clustering. To improve the performance of time series clustering, various methods of metric learning can be applied to generate a proper distance metric from the data. However, the existing metric learning methods overlook the characteristics of time series. And for time series, it is difficult to obtain side information, such as pairwise constraints, for metric learning. In this paper, a method for distance metric learning based on side information autogeneration for time series (SIADML) is proposed. In this method, dynamic time warping (DTW) distance is used to measure the similarity between two time series and generate pairwise constraints automatically. The metric which is learned from the pairwise constraints can preserve the neighbor relationship of time series as much as possible. Experimental results on benchmark datasets demonstrate that the proposed method can effectively improve the performance for time series clustering.

    参考文献
    [1] Xing EP, Andrew YN, Jordan M, Russell S. Distance metric learning with application to clustering with side-information. In: Advances in Neural Information Processing Systems 15. The MIT Press, 2002. 505-512.
    [2] Shental N, Hertz T, Weinshall D, Pavel M. Adjustment learning and relevant component analysis. In: Proc. of the European Conf. on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2002. 776-792.[doi: 10.1007/3-540-47979-1_52]
    [3] Bar-Hillel A, Hertz T, Shental N, Weinshall D. Learning a Mahalanobis metric from equivalence constraints. Journal of Machine Learning Research, 2005,6(6):937-965.
    [4] Hoi SCH, Liu W, Lyu MR, Ma WY. Learning distance metrics with contextual constraints for image retrieval. In: Proc. of the Conf. on Computer Vision and Pattern Recognition. IEEE Computer Society, 2006. 2072-2078.[doi: 10.1109/CVPR.2006.167]
    [5] Xiang S, Nie F, Zhang C. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition, 2008, 41(12):3600-3612.[doi: 10.1016/j.patcog.2008.05.018]
    [6] Baghshah MS, Shouraki SB. Semi-Supervised metric learning using pairwise constraints. In: Proc. of the Int''l Joint Conf. on Artificial Intelligence. AAAI Press, 2009. 1217-1222.
    [7] Wang QY, Yuen PC, Feng GC. Semi-Supervised metric learning via topology preserving multiple semi-supervised assumptions. Pattern Recognition, 2013,46(9):2576-2587.[doi: 10.1016/j.patcog.2013.02.015]
    [8] Zhang Z, Chow TWS, Zhao MB. Trace ratio optimization-based semi-supervised nonlinear dimensionality reduction for marginal manifold visualization. IEEE Trans. on Knowledge and Data Engineering, 2013,25(5):1148-1161.[doi: 10.1109/TKDE.2012.47]
    [9] Jun W, Do H, Woznica A, Kalousis A. Metric learning with multiple kernels. In: Advances in Neural Information Processing Systems 24. 2011. 1170-1178.
    [10] Jain P, Kulis B, Davis JV, Dhillon IS. Metric and kernel learning using a linear transformation. Journal of Machine Learning Research, 2012,13:519-547.
    [11] Guillaumin M, Verbeek J, Schmid C. Is that you? Metric learning approaches for face identification. In: Proc. of the Int''l Conf. on Computer Vision. IEEE Computer Society, 2009. 498-505.[doi: 10.1109/ICCV.2009.5459197]
    [12] Yang L, Jin R, Mummert L, Sukthankar R, Goode A, Bin Z, Hoi SC, Satyanarayanan M. A boosting framework for visualitypreserving distance metric learning and its application to medical image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010,32(1):30-44.[doi: 10.1109/TPAMI.2008.273]
    [13] Tran D, Sorokin A. Human activity recognition with metric learning. In: Proc. of the European Conf. on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2008. 548-561.[doi: 10.1007/978-3-540-88682-2_42]
    [14] Prekopcsák Z, Lemire D. Time series classification by class-specific Mahalanobis distance measures. Advances in Data Analysis and Classification, 2012,6(3):185-200.[doi: 10.1007/s11634-012-0110-6]
    [15] Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. on Acoustics, Speech and Signal Processing, 1978,26(1):43-49.[doi: 10.1109/TASSP.1978.1163055]
    [16] Xi X, Keogh E, Shelton C, Wei L, Ratanamahatana CA. Fast time series classification using numerosity reduction. In: Proc. of the Int''l Conf. on Machine Learning. ACM, 2006. 1033-1040.[doi: 10.1145/1143844.1143974]
    [17] Rakthanmanon T, Campana BJ, Mueen A, Batista G, Westover B, Qiang Z, Zakaria J, Keogh E. Searching and mining trillions of time series subsequences under dynamic time warping. In: Proc. of the Int''l Conf. on Knowledge Discovery and Data Mining. ACM, 2012. 262-270.[doi: 10.1145/2339530.2339576]
    [18] Keogh E. Exact indexing of dynamic time warping. In: Proc. of the Int''l Conf. on Very Large Databases. 2002. 406-417.[doi: 10.1007/s10115-004-0154-9]
    [19] Shimodaira H, Noma K, Nakai M, Sagayama S. Dynamic time-alignment kernel in support vector machine. In: Advances in Neural Information Processing Systems 15. The MIT Press, 2002. 921-928.
    [20] Cuturi M, Vert JP, Birkenes O, Matsui T. A kernel for time series based on global alignments. In: Proc. of the Int''l Conf. on Acoustics, Speech and Signal Processing. IEEE Computer Society, 2007. 413-416.[doi: 10.1109/ICASSP.2007.366260]
    [21] Cuturi M. Fast global alignment kernels. In: Proc. of the Int''l Conf. on Machine Learning. ACM, 2011. 929-936.
    [22] Zhou F, De la Torre F, Cohn JF. Unsupervised discovery of facial events. In: Proc. of the Conf. on Computer Vision and Pattern Recognition. IEEE Computer Society, 2010. 2574-2581.[doi: 10.1109/CVPR.2010.5539966]
    [23] Zhou F, De la Torre F, Hodgins J. Hierarchical aligned cluster analysis for temporal clustering of human motion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2013,35(3):582-596.[doi: 10.1109/TPAMI.2012.137]
    [24] Ratanamahatana CA, Keogh E. Three myths about dynamic time warping data mining. In: Proc. of the SIAM Int''l Conf. on Data Mining. SDM, 2005. 506-510.
    [25] Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000,290(5500):2323-2326.[doi: 10.1126/science.290.5500.2323]
    [26] Domeniconi C, Gunopulos D. Adaptive nearest neighbor classification using support vector machines. In: Advances in Neural Information Processing Systems 14. The MIT Press, 2001. 665-672.
    [27] Feng YC, Jiang T, Li GH, Zhu H. Underlying techniques of efficient similarity search on time series. Chinese Journal of Computers, 2009,32(11):2107-2122 (in Chinese with English abstract).[doi: 10.3724/sp.j.1016.2009.02107]
    [28] Kim S, Park S, Chu W. An index-based approach for similarity search supporting time warping in large sequence databases. In: Proc. of the Int''l Conf. of Data Engineering. IEEE Computer Society, 2001. 607-614.[doi: 10.1109/ICDE.2001.914875]
    [29] Zhou M, Wong MH. Efficient online subsequence searching in data streams under dynamic time warping distance. In: Proc. of the Int''l Conf. of Data Engineering. IEEE Computer Society, 2008. 686-695.[doi: 10.1109/ICDE.2008.4497477]
    [30] Keogh E, Zhu Q, Hu B, Hao Y, Xi X, Wei L, Ratanamahatana CA. The UCR time series classification/clustering homepage. 2011. http://www.cs.ucr.edu/~eamonn/time_series_data/
    [31] Settles B. Active learning literature survey. Technical Report, 1648, University of Wisconsin-Madison, 2009.
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邹朋成,王建东,杨国庆,张霞,王丽娜.辅助信息自动生成的时间序列距离度量学习.软件学报,2013,24(11):2642-2655

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  • 收稿日期:2013-01-06
  • 最后修改日期:2013-08-02
  • 在线发布日期: 2013-11-01
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