Shape averaging under Time Warping

机译：时间扭曲下的形状平均

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摘要

Dynamic time warping (DTW) distance measure has increasingly been used as a similarity measurement for various data mining tasks in place of traditional Euclidean distance metric due to its superiority in sequence-alignment flexibility. However, in some tasks where shape averaging is required, e.g., in template matching and k-means clustering problems, current averaging methods are inaccurate in that they produce undesired templates and cluster representatives. In this work, we emphasize the importance of the correctness of this averaging subroutine and propose a novel shape averaging method, called prioritized shape averaging (PSA), using hierarchical clustering approach. In experimental evaluation, our proposed method, PSA, achieves a lower discrepancy distance between an averaged sequence and every original sequence than existing method on various domains.

机译：动态时间规整（DTW）距离度量由于其序列比对灵活性的优越性，已越来越多地用作各种数据挖掘任务的相似性度量，以代替传统的欧几里得距离度量。然而，在一些需要形状平均的任务中，例如在模板匹配和k均值聚类问题中，当前的平均方法是不准确的，因为它们会产生不希望的模板和聚类代表。在这项工作中，我们强调此平均子程序正确性的重要性，并提出一种使用分层聚类方法的新颖形状平均方法，称为优先形状平均（PSA）。在实验评估中，我们提出的方法PSA与各种领域的现有方法相比，平均序列与每个原始序列之间的差异距离更小。

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《International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology;ECTI-CON 2009》|2009年|626-629|共4页
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Niennattrakul V.; Ratanamahatana C.A.;
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data mining; pattern clustering; pattern matching; time series; DTW; Euclidean distance metric; dynamic time warping; hierarchical clustering approach; k-means clustering; prioritized shape averaging; sequence-alignment flexibility; similarity measurement; template matching;

机译：数据挖掘;模式聚类;模式匹配;时间序列; DTW;欧氏距离度量;动态时间规整;分层聚类方法; k-均值聚类;优先形状平均;序列对齐灵活性;相似性测量;模板匹配;

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1. WARP: accurate retrieval of shapes using phase of Fourier descriptors and time warping distance [J] . Bartolini I., Ciaccia P., Patella M. IEEE Transactions on Pattern Analysis and Machine Intelligence . 2005,第1期

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2. A novel non-parametric method for time series classification based on k-Nearest Neighbors and Dynamic Time Warping Barycenter Averaging [J] . Tuan Minh Tran, Xuan-May Thi Le, Nguyen Hien T., Engineering Applications of Artificial Intelligence . 2019,第FEBa期

机译：基于k最近邻和动态时间规整重心平均的非时间序列分类新方法
3. Revisiting inaccuracies of time series averaging under dynamic time warping [J] . Jain Brijnesh Pattern recognition letters . 2019,第JULa期

机译：重新探讨动态时间扭曲下时间序列平均的不准确性
4. Contrast enhanced dynamic time warping distance for time series shape averaging classification [C] . Songpol Ongwattanakul, Dararat Srisai 2nd international conference on interaction sciences : Information technology, culture and human 2009 . 2009

机译：对比度增强的动态时间扭曲距离，用于时间序列形状平均分类
5. Regularization of Dynamic Time Warping Barycenter Averaging, with Applications in Sign Classification. [D] . Costanzo, John A.W.B. 2013

机译：动态时间规整重心平均的正则化及其在符号分类中的应用。
6. Time Warp: Authorship Shapes the Perceived Timing of Actions and Events [O] . Jeffrey P. Ebert, Daniel M. Wegner -1

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7. Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data [O] . Vit Niennattrakul, Chotirat Ann Ratanamahatana 2008

机译：时间序列数据使用动态时间规整的形状平均方法的不准确性
8. Probe shapes that measure time-averaged streamwise momentum and cross-stream turbulence intensity [R] . 1993

机译：探测形状，测量时间平均的流向动量和交叉流湍流强度

Shape averaging under Time Warping

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