Robust Shapelets Learning:Transform-Invariant Prototypes

摘要：

　　Shapelets are discriminative local patterns in time series,which maximally distinguish among different classes.Instead of considering full series,shapelet transformation considers the existence or absence of local shapelets,which leads to high classification accuracy,easy visualization and interpretability.One of the limitation of existing methods is robustness.For example,Search-based approaches select sample subsequences as shapelets and those methods intuitively may be not accurate and robust enough.Learning-based approaches learn shapelets by maximizing the discriminative ability.However,those methods may not preserve basic shape for visualization.In practice,shapelets are subjected to various geometric transformations,such as translation,scaling,and stretching,which may result in a confusion of shapelet judgement.In this paper,robust shapelet learning is proposed to solve above problems.By learning transform-invariant representative prototypes from all training time series,rather than just selecting samples from the sequences,each time series sample could be approximated by the combination of the transformations of those prototypes.Based on the combination,samples could be easily classified into different classes.Experiments on 16 UCR time series datasets showed that the performance of the proposed framework is comparable to the state-of-art methods,but could learn more representative shapelets for complex scenarios.

关键词： Robustness Transform-invariant Representative prototype

作者: Huiqi Deng Weifu Chen Andy J.Ma Qi Shen Pong C.Yuen Guocan Feng

作者单位: School of Mathematics,Sun Yat-sen University,Guangzhou,China;Department of Computer Science,Hong Kon School of Mathematics,Sun Yat-sen University,Guangzhou,China School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China;Department of Computer Sc School of Mathematics,Sun Yat-sen University,Guangzhou,China;Department of Genetics and Genomic Scie Department of Computer Science,Hong Kong Baptist University,Hong Kong,China

会议类型: 国际会议

会议名称: 中国模式识别与计算机视觉大会(PRCV2018)

会议地点: 广州

会议语种:英文

页码: 491-502

在线出版日期: 2018-11-23（万方平台首次上网日期，不代表论文的发表时间）